From 208f2f2dad6aa1a96a7486c38bd8ef5370368a3a Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:28:04 +0200 Subject: [PATCH 01/38] Rename notebook: hamiltonian simulation guide --- tests/resources/timeouts.yaml | 6 +++--- .../hamiltonian_simulation_guide.ipynb | 6 +++--- ...miltonian_simulation_guide_exponentiation.metadata.json} | 0 ...mod => hamiltonian_simulation_guide_exponentiation.qmod} | 0 ..._simulation_guide_exponentiation.synthesis_options.json} | 0 ...on => hamiltonian_simulation_guide_qdrift.metadata.json} | 0 ...qdrift.qmod => hamiltonian_simulation_guide_qdrift.qmod} | 0 ...iltonian_simulation_guide_qdrift.synthesis_options.json} | 0 ...n => hamiltonian_simulation_guide_trotter.metadata.json} | 0 ...otter.qmod => hamiltonian_simulation_guide_trotter.qmod} | 0 ...ltonian_simulation_guide_trotter.synthesis_options.json} | 0 11 files changed, 6 insertions(+), 6 deletions(-) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{exponentiation.metadata.json => hamiltonian_simulation_guide_exponentiation.metadata.json} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{exponentiation.qmod => hamiltonian_simulation_guide_exponentiation.qmod} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{exponentiation.synthesis_options.json => hamiltonian_simulation_guide_exponentiation.synthesis_options.json} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{qdrift.metadata.json => hamiltonian_simulation_guide_qdrift.metadata.json} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{qdrift.qmod => hamiltonian_simulation_guide_qdrift.qmod} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{qdrift.synthesis_options.json => hamiltonian_simulation_guide_qdrift.synthesis_options.json} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{trotter.metadata.json => hamiltonian_simulation_guide_trotter.metadata.json} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{trotter.qmod => hamiltonian_simulation_guide_trotter.qmod} (100%) rename tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/{trotter.synthesis_options.json => hamiltonian_simulation_guide_trotter.synthesis_options.json} (100%) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 1f2ad0289..2a57cb661 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -290,10 +290,10 @@ tutorials/popular_usage_examples/basic_tutorials/prepare_state/prepare_state.qmo tutorials/popular_usage_examples/exponentiation/exponentiation.ipynb: 216 tutorials/popular_usage_examples/exponentiation/exponentiation.qmod: 92 tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.qmod: 20 -tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.qmod: 300 +tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.qmod: 300 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb: 1000 -tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.qmod: 300 -tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.qmod: 300 +tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.qmod: 300 +tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.qmod: 300 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qsvt.qmod: 300 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qubitization.qmod: 300 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_with_block_encoding.ipynb: 600 diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb index fc6f93492..09ae1e071 100644 --- a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb +++ b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb @@ -187,7 +187,7 @@ "\n", "qmod = create_model(main)\n", "qprog = synthesize(qmod)\n", - "write_qmod(qmod, \"trotter\")\n", + "write_qmod(qmod, \"hamiltonian_simulation_guide_trotter\")\n", "show(qprog)" ] }, @@ -232,7 +232,7 @@ "\n", "\n", "qmod = create_model(main)\n", - "write_qmod(qmod, \"exponentiation\")\n", + "write_qmod(qmod, \"hamiltonian_simulation_guide_exponentiation\")\n", "qprog = synthesize(qmod)\n", "show(qprog)" ] @@ -327,7 +327,7 @@ "\n", "\n", "qmod = create_model(main)\n", - "write_qmod(qmod, \"qdrift\")\n", + "write_qmod(qmod, \"hamiltonian_simulation_guide_qdrift\")\n", "qprog = synthesize(qmod)\n", "show(qprog)" ] diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.metadata.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.metadata.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.metadata.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.metadata.json diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.qmod b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.qmod similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.qmod rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.qmod diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.synthesis_options.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.synthesis_options.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/exponentiation.synthesis_options.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.synthesis_options.json diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.metadata.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.metadata.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.metadata.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.metadata.json diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.qmod b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.qmod similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.qmod rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.qmod diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.synthesis_options.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.synthesis_options.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/qdrift.synthesis_options.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.synthesis_options.json diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.metadata.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.metadata.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.metadata.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.metadata.json diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.qmod b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.qmod similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.qmod rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.qmod diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.synthesis_options.json b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.synthesis_options.json similarity index 100% rename from tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/trotter.synthesis_options.json rename to tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_trotter.synthesis_options.json From 1bbf05a804e4f7b5144b98ba116f164586eb98c6 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:32:01 +0200 Subject: [PATCH 02/38] Rename notebook: hamiltonian evolution --- ...tiation.ipynb => hamiltonian_evolution_exponentiation.ipynb} | 2 +- ....json => hamiltonian_evolution_exponentiation.metadata.json} | 0 ...entiation.qmod => hamiltonian_evolution_exponentiation.qmod} | 0 ...hamiltonian_evolution_exponentiation.synthesis_options.json} | 0 4 files changed, 1 insertion(+), 1 deletion(-) rename functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/{exponentiation.ipynb => hamiltonian_evolution_exponentiation.ipynb} (98%) rename functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/{exponentiation.metadata.json => hamiltonian_evolution_exponentiation.metadata.json} (100%) rename functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/{exponentiation.qmod => hamiltonian_evolution_exponentiation.qmod} (100%) rename functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/{exponentiation.synthesis_options.json => hamiltonian_evolution_exponentiation.synthesis_options.json} (100%) diff --git a/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.ipynb b/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.ipynb similarity index 98% rename from functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.ipynb rename to functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.ipynb index 9f898d515..dd6d57046 100644 --- a/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.ipynb +++ b/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.ipynb @@ -96,7 +96,7 @@ " )\n", "\n", "\n", - "qmod = create_model(main, out_file=\"exponentiation\")\n", + "qmod = create_model(main, out_file=\"hamiltonian_evolution_exponentiation\")\n", "qprog = synthesize(qmod)" ] }, diff --git a/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.metadata.json b/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.metadata.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.metadata.json rename to functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.metadata.json diff --git a/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.qmod b/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.qmod similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.qmod rename to functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.qmod diff --git a/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.synthesis_options.json b/functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.synthesis_options.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.synthesis_options.json rename to functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.synthesis_options.json From 8ce9ec897e9089ffea9397a1e1ac06faa4212f84 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:33:57 +0200 Subject: [PATCH 03/38] Rename notebook: exponentiation example --- tests/resources/timeouts.yaml | 6 +++--- .../{exponentiation.ipynb => example_exponentiation.ipynb} | 4 ++-- ...n.metadata.json => example_exponentiation.metadata.json} | 0 .../{exponentiation.qmod => example_exponentiation.qmod} | 0 ...s.json => example_exponentiation.synthesis_options.json} | 0 ... => example_exponentiation_minimize_error.metadata.json} | 0 ...rror.qmod => example_exponentiation_minimize_error.qmod} | 0 ...le_exponentiation_minimize_error.synthesis_options.json} | 0 8 files changed, 5 insertions(+), 5 deletions(-) rename tutorials/popular_usage_examples/exponentiation/{exponentiation.ipynb => example_exponentiation.ipynb} (98%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation.metadata.json => example_exponentiation.metadata.json} (100%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation.qmod => example_exponentiation.qmod} (100%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation.synthesis_options.json => example_exponentiation.synthesis_options.json} (100%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation_minimize_error.metadata.json => example_exponentiation_minimize_error.metadata.json} (100%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation_minimize_error.qmod => example_exponentiation_minimize_error.qmod} (100%) rename tutorials/popular_usage_examples/exponentiation/{exponentiation_minimize_error.synthesis_options.json => example_exponentiation_minimize_error.synthesis_options.json} (100%) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 2a57cb661..f6e532812 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -287,9 +287,9 @@ tutorials/popular_usage_examples/basic_tutorials/bernstein_vazirani/bernstein_va tutorials/popular_usage_examples/basic_tutorials/bernstein_vazirani/bv_tutorial.qmod: 30 tutorials/popular_usage_examples/basic_tutorials/prepare_state/prepare_state.ipynb: 20 tutorials/popular_usage_examples/basic_tutorials/prepare_state/prepare_state.qmod: 20 -tutorials/popular_usage_examples/exponentiation/exponentiation.ipynb: 216 -tutorials/popular_usage_examples/exponentiation/exponentiation.qmod: 92 -tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.qmod: 20 +tutorials/popular_usage_examples/exponentiation/example_exponentiation.ipynb: 216 +tutorials/popular_usage_examples/exponentiation/example_exponentiation.qmod: 92 +tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.qmod: 20 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_exponentiation.qmod: 300 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.ipynb: 1000 tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide_qdrift.qmod: 300 diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation.ipynb b/tutorials/popular_usage_examples/exponentiation/example_exponentiation.ipynb similarity index 98% rename from tutorials/popular_usage_examples/exponentiation/exponentiation.ipynb rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation.ipynb index 275b353a4..4b2430d82 100644 --- a/tutorials/popular_usage_examples/exponentiation/exponentiation.ipynb +++ b/tutorials/popular_usage_examples/exponentiation/example_exponentiation.ipynb @@ -157,7 +157,7 @@ " custom_hardware_settings=CustomHardwareSettings(basis_gates=[\"cx\", \"u\"])\n", " ),\n", ")\n", - "write_qmod(qmod, \"exponentiation\")\n", + "write_qmod(qmod, \"example_exponentiation\")\n", "\n", "qprog = synthesize(qmod)\n", "circuit = QuantumProgram.from_qprog(qprog)\n", @@ -248,7 +248,7 @@ "qmod = create_model(main)\n", "\n", "\n", - "write_qmod(qmod, \"exponentiation_minimize_error\")\n", + "write_qmod(qmod, \"example_exponentiation_minimize_error\")\n", "\n", "qprog = synthesize(qmod)\n", "show(qprog)" diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation.metadata.json b/tutorials/popular_usage_examples/exponentiation/example_exponentiation.metadata.json similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation.metadata.json rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation.metadata.json diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation.qmod b/tutorials/popular_usage_examples/exponentiation/example_exponentiation.qmod similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation.qmod rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation.qmod diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation.synthesis_options.json b/tutorials/popular_usage_examples/exponentiation/example_exponentiation.synthesis_options.json similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation.synthesis_options.json rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation.synthesis_options.json diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.metadata.json b/tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.metadata.json similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.metadata.json rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.metadata.json diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.qmod b/tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.qmod similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.qmod rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.qmod diff --git a/tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.synthesis_options.json b/tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.synthesis_options.json similarity index 100% rename from tutorials/popular_usage_examples/exponentiation/exponentiation_minimize_error.synthesis_options.json rename to tutorials/popular_usage_examples/exponentiation/example_exponentiation_minimize_error.synthesis_options.json From f66d2ffae8ead9e9ad2642fd567b471a0dc69f95 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:38:38 +0200 Subject: [PATCH 04/38] Rename notebook: hhl example --- tests/resources/timeouts.yaml | 2 +- .../hhl/{hhl.ipynb => hhl_example.ipynb} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename tutorials/technology_demonstrations/hhl/{hhl.ipynb => hhl_example.ipynb} (100%) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index f6e532812..2aac246b0 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -313,7 +313,7 @@ tutorials/technology_demonstrations/hamiltonian_evolution/hamiltonian_evolution. tutorials/technology_demonstrations/hardware_aware_mcx/hardware_aware_mcx.ipynb: 56 tutorials/technology_demonstrations/hardware_aware_mcx/hardware_aware_mcx_all_to_all.qmod: 36 tutorials/technology_demonstrations/hardware_aware_mcx/hardware_aware_mcx_linear.qmod: 44 -tutorials/technology_demonstrations/hhl/hhl.ipynb: 800 +tutorials/technology_demonstrations/hhl/hhl_example.ipynb: 800 tutorials/technology_demonstrations/oracle_generation/3sat_oracles.ipynb: 1800 tutorials/technology_demonstrations/qaoa/qaoa.ipynb: 450 tutorials/technology_demonstrations/qpe/qpe_for_grover_operator/qpe_for_grover_operator.ipynb: 1000 diff --git a/tutorials/technology_demonstrations/hhl/hhl.ipynb b/tutorials/technology_demonstrations/hhl/hhl_example.ipynb similarity index 100% rename from tutorials/technology_demonstrations/hhl/hhl.ipynb rename to tutorials/technology_demonstrations/hhl/hhl_example.ipynb From a9903355d02e63936271389aeb5bd057e3d995a3 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:40:24 +0200 Subject: [PATCH 05/38] Rename notebook: application option pricing --- ...{option_pricing.ipynb => application_option_pricing.ipynb} | 2 +- ...metadata.json => application_option_pricing.metadata.json} | 0 .../{option_pricing.qmod => application_option_pricing.qmod} | 0 ...json => application_option_pricing.synthesis_options.json} | 0 tests/resources/timeouts.yaml | 4 ++-- 5 files changed, 3 insertions(+), 3 deletions(-) rename built_in_apps/option_pricing/{option_pricing.ipynb => application_option_pricing.ipynb} (99%) rename built_in_apps/option_pricing/{option_pricing.metadata.json => application_option_pricing.metadata.json} (100%) rename built_in_apps/option_pricing/{option_pricing.qmod => application_option_pricing.qmod} (100%) rename built_in_apps/option_pricing/{option_pricing.synthesis_options.json => application_option_pricing.synthesis_options.json} (100%) diff --git a/built_in_apps/option_pricing/option_pricing.ipynb b/built_in_apps/option_pricing/application_option_pricing.ipynb similarity index 99% rename from built_in_apps/option_pricing/option_pricing.ipynb rename to built_in_apps/option_pricing/application_option_pricing.ipynb index ace23709e..762e4dff5 100644 --- a/built_in_apps/option_pricing/option_pricing.ipynb +++ b/built_in_apps/option_pricing/application_option_pricing.ipynb @@ -165,7 +165,7 @@ }, "outputs": [], "source": [ - "write_qmod(qmod, \"option_pricing\")" + "write_qmod(qmod, \"application_option_pricing\")" ] }, { diff --git a/built_in_apps/option_pricing/option_pricing.metadata.json b/built_in_apps/option_pricing/application_option_pricing.metadata.json similarity index 100% rename from built_in_apps/option_pricing/option_pricing.metadata.json rename to built_in_apps/option_pricing/application_option_pricing.metadata.json diff --git a/built_in_apps/option_pricing/option_pricing.qmod b/built_in_apps/option_pricing/application_option_pricing.qmod similarity index 100% rename from built_in_apps/option_pricing/option_pricing.qmod rename to built_in_apps/option_pricing/application_option_pricing.qmod diff --git a/built_in_apps/option_pricing/option_pricing.synthesis_options.json b/built_in_apps/option_pricing/application_option_pricing.synthesis_options.json similarity index 100% rename from built_in_apps/option_pricing/option_pricing.synthesis_options.json rename to built_in_apps/option_pricing/application_option_pricing.synthesis_options.json diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 2aac246b0..84d0fe509 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -120,8 +120,8 @@ built_in_apps/chemistry/chemistry.ipynb: 48 built_in_apps/chemistry/chemistry.qmod: 48 built_in_apps/grover/grover.ipynb: 48 built_in_apps/grover/grover.qmod: 48 -built_in_apps/option_pricing/option_pricing.ipynb: 48 -built_in_apps/option_pricing/option_pricing.qmod: 48 +built_in_apps/option_pricing/application_option_pricing.ipynb: 48 +built_in_apps/option_pricing/application_option_pricing.qmod: 48 community/QClass_2024/Assignments/HW1_QClass2024.ipynb: 200 community/QClass_2024/Assignments/HW2_QClass2024.ipynb: 200 community/QClass_2024/Assignments/Preparation_for_Week2_Git_GitHub.ipynb: 20 From 8138e8b477990671d094e43ee6108ffdb263ad4d Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 10:44:32 +0200 Subject: [PATCH 06/38] Rename notebook: example prepare state --- ...metadata.json => example_prepare_amplitudes.metadata.json} | 0 ...repare_amplitudes.qmod => example_prepare_amplitudes.qmod} | 0 ...json => example_prepare_amplitudes.synthesis_options.json} | 0 ...tate.metadata.json => example_prepare_state.metadata.json} | 0 .../{prepare_state.qmod => example_prepare_state.qmod} | 0 ...ions.json => example_prepare_state.synthesis_options.json} | 0 .../prepare_state_and_amplitudes.ipynb | 4 ++-- tests/resources/timeouts.yaml | 4 ++-- 8 files changed, 4 insertions(+), 4 deletions(-) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_amplitudes.metadata.json => example_prepare_amplitudes.metadata.json} (100%) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_amplitudes.qmod => example_prepare_amplitudes.qmod} (100%) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_amplitudes.synthesis_options.json => example_prepare_amplitudes.synthesis_options.json} (100%) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_state.metadata.json => example_prepare_state.metadata.json} (100%) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_state.qmod => example_prepare_state.qmod} (100%) rename functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/{prepare_state.synthesis_options.json => example_prepare_state.synthesis_options.json} (100%) diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.metadata.json b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.metadata.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.metadata.json rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.metadata.json diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.qmod b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.qmod similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.qmod rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.qmod diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.synthesis_options.json b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.synthesis_options.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.synthesis_options.json rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.synthesis_options.json diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.metadata.json b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.metadata.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.metadata.json rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.metadata.json diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.qmod b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.qmod similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.qmod rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.qmod diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.synthesis_options.json b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.synthesis_options.json similarity index 100% rename from functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.synthesis_options.json rename to functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.synthesis_options.json diff --git a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state_and_amplitudes.ipynb b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state_and_amplitudes.ipynb index 749d2aad1..e8f3788b1 100644 --- a/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state_and_amplitudes.ipynb +++ b/functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state_and_amplitudes.ipynb @@ -129,7 +129,7 @@ " prepare_state(probabilities=probabilities, bound=0.01, out=x)\n", "\n", "\n", - "qmod = create_model(main, out_file=\"prepare_state\")" + "qmod = create_model(main, out_file=\"example_prepare_state\")" ] }, { @@ -211,7 +211,7 @@ " num_shots=1,\n", " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator_statevector\"),\n", ")\n", - "write_qmod(qmod, \"prepare_amplitudes\", decimal_precision=15)" + "write_qmod(qmod, \"example_prepare_amplitudes\", decimal_precision=15)" ] }, { diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 84d0fe509..eb8ed1621 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -223,8 +223,8 @@ functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/qdrift/ functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/qdrift/qdrift.qmod: 10 functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/suzuki_trotter/suzuki_trotter.ipynb: 20 functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/suzuki_trotter/suzuki_trotter.qmod: 10 -functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_amplitudes.qmod: 10 -functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state.qmod: 10 +functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_amplitudes.qmod: 10 +functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/example_prepare_state.qmod: 10 functions/qmod_library_reference/qmod_core_library/prepare_state_and_amplitudes/prepare_state_and_amplitudes.ipynb: 20 functions/qmod_library_reference/qmod_core_library/standard_gates/CRX.qmod: 10 functions/qmod_library_reference/qmod_core_library/standard_gates/CX.qmod: 10 From 7973caa2849b26ac66ed6baa5d894081811f7cca Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:09:41 +0200 Subject: [PATCH 07/38] Fix rename --- tests/resources/timeouts.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index eb8ed1621..133b3afba 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -217,8 +217,8 @@ functions/qmod_library_reference/classiq_open_library/special_state_preparations functions/qmod_library_reference/classiq_open_library/variational_data_encoding/encode_in_angle.qmod: 10 functions/qmod_library_reference/classiq_open_library/variational_data_encoding/encode_on_bloch.qmod: 10 functions/qmod_library_reference/classiq_open_library/variational_data_encoding/variational_data_encoding.ipynb: 20 -functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.ipynb: 20 -functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/exponentiation.qmod: 10 +functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.ipynb: 20 +functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/exponentiation/hamiltonian_evolution_exponentiation.qmod: 10 functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/qdrift/qdrift.ipynb: 20 functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/qdrift/qdrift.qmod: 10 functions/qmod_library_reference/qmod_core_library/hamiltonian_evolution/suzuki_trotter/suzuki_trotter.ipynb: 20 From dfbbf7c068cee62885633d1454c0b27d6e934b66 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:28:55 +0200 Subject: [PATCH 08/38] Add test for unique notebook names --- tests/internal/test_notebook_unique_name.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 tests/internal/test_notebook_unique_name.py diff --git a/tests/internal/test_notebook_unique_name.py b/tests/internal/test_notebook_unique_name.py new file mode 100644 index 000000000..d73e8ab39 --- /dev/null +++ b/tests/internal/test_notebook_unique_name.py @@ -0,0 +1,19 @@ +from pathlib import Path + + +ROOT = Path(__file__).parents[2] + + +def test_unique_notebook_name(): + all_notebooks = ROOT.rglob("*.ipynb") + assert _are_all_base_names_unique(all_notebooks) + + +def test_unique_qmod_name(): + all_qmods = ROOT.rglob("*.qmod") + assert _are_all_base_names_unique(all_qmods) + + +def _are_all_base_names_unique(files: Iterable[Path]) -> bool: + base_names = [f.name for f in files] + return len(base_names) == len(set(base_names)) From 12d12f80ee9dc47879d5de698f804e756017a8c3 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:30:02 +0200 Subject: [PATCH 09/38] Update timeouts to be relative paths --- tests/resources/timeouts.yaml | 658 +++++++++++++++++----------------- 1 file changed, 329 insertions(+), 329 deletions(-) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 133b3afba..1305243ce 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -1,329 +1,329 @@ -algorithms/dqi/dqi_max_xorsat.ipynb: 200 -algorithms/dqi/dqi_max_xorsat.qmod: 200 -algorithms/algebraic/discrete_log/discrete_log.ipynb: 600 -algorithms/algebraic/discrete_log/discrete_log.qmod: 300 -algorithms/algebraic/discrete_log/discrete_log_large.qmod: 600 -algorithms/algebraic/hidden_shift/hidden_shift.ipynb: 272 -algorithms/algebraic/hidden_shift/hidden_shift_complex.qmod: 100 -algorithms/algebraic/hidden_shift/hidden_shift_no_dual.qmod: 92 -algorithms/algebraic/hidden_shift/hidden_shift_simple.qmod: 20 -algorithms/algebraic/shor/doubly_controlled_modular_adder.qmod: 100 -algorithms/algebraic/shor/shor.ipynb: 104 -algorithms/algebraic/shor/shor.qmod: 88 -algorithms/algebraic/shor/shor_modular_exponentiation.ipynb: 300 -algorithms/algebraic/shor/shor_modular_exponentiation.qmod: 300 -algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.ipynb: 176 -algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.qmod: 136 -algorithms/amplitude_estimation/quantum_counting/quantum_counting.ipynb: 300 -algorithms/amplitude_estimation/quantum_counting/quantum_counting_iqae.qmod: 200 -algorithms/amplitude_estimation/quantum_counting/quantum_counting_qpe.qmod: 200 -algorithms/bernstein_vazirani/bernstein_vazirani.ipynb: 32 -algorithms/bernstein_vazirani/bernstein_vazirani_example.qmod: 32 -algorithms/deutsch_jozsa/complex_deutsch_jozsa.qmod: 32 -algorithms/deutsch_jozsa/deutsch_jozsa.ipynb: 48 -algorithms/deutsch_jozsa/simple_deutsch_jozsa.qmod: 16 -algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.ipynb: 300 -algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.qmod: 300 -algorithms/differential_equations/hhl_jungle/hhl_jungle.ipynb: 450 -algorithms/grover/3_sat_grover/3_sat_grover.ipynb: 36 -algorithms/grover/3_sat_grover/3_sat_grover.qmod: 48 -algorithms/grover/3_sat_grover/3_sat_grover_large.qmod: 10 -algorithms/grover/grover_max_cut/grover_max_cut.ipynb: 220 -algorithms/grover/grover_max_cut/grover_max_cut.qmod: 188 -algorithms/hhl/hhl/hhl.ipynb: 312 -algorithms/hhl/hhl/hhl_exact.qmod: 100 -algorithms/hhl/hhl/hhl_trotter.qmod: 100 -algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.ipynb: 20 -algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.qmod: 10 -algorithms/qml/hybrid_qnn/hybrid_qnn_for_subset_majority.ipynb: 120 -algorithms/qml/qgan/qgan_bars_and_strips.ipynb: 360 -algorithms/qml/qsvm/qsvm.ipynb: 204 -algorithms/qml/qsvm/qsvm.qmod: 104 -algorithms/qml/qsvm_pauli_feature_map/qsvm_pauli_feature_map.ipynb: 68 -algorithms/qml/qsvm_pauli_feature_map/qsvm_pauli_feature_map.qmod: 60 -algorithms/qml/quantum_autoencoder/quantum_autoencoder.ipynb: 120 -algorithms/qpe/qpe_for_matrix/qpe_for_matrix.ipynb: 748 -algorithms/qpe/qpe_for_matrix/qpe_for_matrix.qmod: 760 -algorithms/qsvt/qsvt_fixed_point_amplitude_amplification/qsvt_fixed_point_amplitude_amplification.ipynb: 376 -algorithms/qsvt/qsvt_fixed_point_amplitude_amplification/qsvt_fixed_point_amplitude_amplification.qmod: 340 -algorithms/qsvt/qsvt_matrix_inversion/qsvt_matrix_inversion.ipynb: 180 -algorithms/qsvt/qsvt_matrix_inversion/qsvt_matrix_inversion.qmod: 156 -algorithms/simon/simon.ipynb: 40 -algorithms/simon/simon_example.qmod: 30 -algorithms/simon/simon_shallow_example.qmod: 20 -algorithms/swap_test/swap_test.ipynb: 40 -algorithms/swap_test/swap_test.qmod: 40 -algorithms/vqls/lcu_vqls/vqls_with_lcu.ipynb: 1200 -algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod: 20 -applications/benchmarking/quantum_volume/quantum_volume.ipynb: 516 -applications/benchmarking/randomized_benchmarking/randomized_benchmarking.ipynb: 60 -applications/chemistry/molecular_energy_curve/molecular_energy_curve.ipynb: 1200 -applications/chemistry/molecular_energy_curve/molecular_energy_curve.qmod: 90 -applications/chemistry/molecule_eigensolver/molecule_eigensolver.ipynb: 84 -applications/chemistry/molecule_eigensolver/molecule_eigensolver.qmod: 60 -applications/chemistry/protein_folding/protein_folding.ipynb: 240 -applications/chemistry/protein_folding/protein_folding.qmod: 240 -applications/chemistry/qpe_for_molecules/qpe_for_molecules.ipynb: 1332 -applications/chemistry/qpe_for_molecules/qpe_for_molecules.qmod: 1292 -applications/chemistry/second_quantized_hamiltonian/second_quantized_hamiltonian.ipynb: 44 -applications/chemistry/second_quantized_hamiltonian/second_quantized_hamiltonian.qmod: 40 -applications/cybersecurity/link_monitoring/link_monitoring.ipynb: 76 -applications/cybersecurity/link_monitoring/link_monitoring.qmod: 88 -applications/cybersecurity/patching_management/patch_min_vertex_cover.qmod: 52 -applications/cybersecurity/patching_management/patching_managment.ipynb: 36 -applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.ipynb: 720 -applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.qmod: 720 -applications/finance/credit_card_fraud/credit_card_fraud.ipynb: 1084 -applications/finance/credit_card_fraud/credit_card_fraud.qmod: 1064 -applications/finance/option_pricing/option_pricing.ipynb: 140 -applications/finance/option_pricing/option_pricing.qmod: 140 -applications/finance/portfolio_optimization/portfolio_optimization.ipynb: 96 -applications/finance/portfolio_optimization/portfolio_optimization.qmod: 112 -applications/logistics/facility_location/facility_location.ipynb: 1656 -applications/logistics/facility_location/facility_location.qmod: 1592 -applications/logistics/task_scheduling_problem/task_scheduling_problem.ipynb: 840 -applications/logistics/task_scheduling_problem/task_scheduling_problem.qmod: 64 -applications/logistics/task_scheduling_problem/task_scheduling_problem_large.qmod: 688 -applications/logistics/traveling_salesman_problem/traveling_saleman_problem.qmod: 1088 -applications/logistics/traveling_salesman_problem/traveling_salesman_problem.ipynb: 1068 -applications/optimization/electric_grid_optimization/electric_grid_optimization.ipynb: 996 -applications/optimization/electric_grid_optimization/electric_grid_optimization.qmod: 1152 -applications/optimization/integer_linear_programming/integer_linear_programming.ipynb: 296 -applications/optimization/integer_linear_programming/integer_linear_programming.qmod: 340 -applications/optimization/knapsack_binary/knapsack_binary.ipynb: 72 -applications/optimization/knapsack_binary/knapsack_binary.qmod: 52 -applications/optimization/knapsack_integer/knapsack_integer.ipynb: 116 -applications/optimization/knapsack_integer/knapsack_integer.qmod: 116 -applications/optimization/max_clique/max_clique.ipynb: 276 -applications/optimization/max_clique/max_clique.qmod: 260 -applications/optimization/max_cut/max_cut.ipynb: 36 -applications/optimization/max_cut/max_cut.qmod: 32 -applications/optimization/max_independent_set/max_independent_set.ipynb: 60 -applications/optimization/max_independent_set/max_independent_set.qmod: 64 -applications/optimization/max_induced_k_color_subgraph/max_induced_k_color_subgraph.ipynb: 1028 -applications/optimization/max_induced_k_color_subgraph/max_induced_k_color_subgraph.qmod: 1008 -applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb: 184 -applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod: 148 -applications/optimization/min_graph_coloring/min_graph_coloring.ipynb: 1800 -applications/optimization/min_graph_coloring/min_graph_coloring.qmod: 1800 -applications/optimization/minimum_dominating_set/minimum_dominating_set.ipynb: 620 -applications/optimization/minimum_dominating_set/minimum_dominating_set.qmod: 588 -applications/optimization/rectangles_packing/rectangles_packing.qmod: 1800 -applications/optimization/rectangles_packing/rectangles_packing_grid.ipynb: 1800 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20 +oblivious_amplitude_amplification.qmod: 10 +optimize.ipynb: 20 +optimize.qmod: 10 +option_pricing.ipynb: 140 +option_pricing.qmod: 140 +Otmane_Ainelkitane_HW3_VQE.ipynb: 30 +part1_arithmetic.ipynb: 30 +part2_state_preparation.ipynb: 30 +part3_deutsch_jozsa.ipynb: 20 +part4_ghz_state.ipynb: 60 +part5_grover.ipynb: 60 +patch_min_vertex_cover.qmod: 52 +patching_managment.ipynb: 36 +PHASE.qmod: 10 +phase_kickback.ipynb: 1000 +phase_kickback.qmod: 1000 +portfolio_optimization.ipynb: 96 +portfolio_optimization.qmod: 112 +Preparation_for_Week2_Git_GitHub.ipynb: 20 +prepare_bell_state.ipynb: 20 +prepare_bell_state.qmod: 10 +prepare_exponential_state.ipynb: 20 +prepare_exponential_state.qmod: 10 +prepare_ghz_state.ipynb: 20 +prepare_ghz_state.qmod: 10 +prepare_int.ipynb: 20 +prepare_int.qmod: 10 +prepare_partial_uniform_state.ipynb: 20 +prepare_state.ipynb: 20 +prepare_state.qmod: 20 +prepare_state_and_amplitudes.ipynb: 20 +prepare_uniform_interval_state.qmod: 10 +prepare_uniform_trimmed_state.qmod: 10 +Priyabrata_Bag_HW4.ipynb: 40 +protein_folding.ipynb: 240 +protein_folding.qmod: 240 +qaoa.ipynb: 450 +qct_qst.ipynb: 30 +qct_qst_type1.qmod: 20 +qct_qst_type2.qmod: 20 +qct_type2.qmod: 20 +qdrift.ipynb: 20 +qdrift.qmod: 10 +qft.ipynb: 20 +qft.qmod: 10 +qgan_bars_and_strips.ipynb: 360 +qmc_user_defined.ipynb: 176 +qmc_user_defined.qmod: 136 +qml_with_classiq_guide.ipynb: 300 +QMOD_Workshop_Part_1.ipynb: 400 +QMOD_Workshop_Part_2.ipynb: 80 +QMOD_Workshop_Part_3.ipynb: 80 +qnn_with_pytorch.qmod: 300 +qpe.ipynb: 20 +qpe.qmod: 10 +qpe_flexible.qmod: 10 +qpe_for_grover_operator.ipynb: 1000 +qpe_for_matrix.ipynb: 748 +qpe_for_matrix.qmod: 760 +qpe_for_molecules.ipynb: 1332 +qpe_for_molecules.qmod: 1292 +qpe_for_unitary_matrix.ipynb: 600 +qst_type2.qmod: 20 +qsvm.ipynb: 204 +qsvm.qmod: 104 +qsvm_pauli_feature_map.ipynb: 68 +qsvm_pauli_feature_map.qmod: 60 +qsvt.ipynb: 20 +qsvt.qmod: 10 +qsvt_fixed_point_amplitude_amplification.ipynb: 376 +qsvt_fixed_point_amplitude_amplification.qmod: 340 +qsvt_matrix_inversion.ipynb: 180 +qsvt_matrix_inversion.qmod: 156 +quantum_autoencoder.ipynb: 120 +quantum_counting.ipynb: 300 +quantum_counting_iqae.qmod: 200 +quantum_counting_qpe.qmod: 200 +quantum_operations.ipynb: 20 +quantum_operations.qmod: 10 +quantum_variables_and_functions.ipynb: 20 +quantum_variables_and_functions.qmod: 10 +quantum_volume.ipynb: 516 +quantum_walk_circle.qmod: 25 +quantum_walk_circle_balanced_coin.qmod: 25 +quantum_walk_hypercube.qmod: 30 +R.qmod: 10 +rainbow_options_bruteforce_method.ipynb: 1000 +rainbow_options_bruteforce_method.qmod: 1000 +rainbow_options_direct_method.ipynb: 1000 +rainbow_options_direct_method.qmod: 1000 +rainbow_options_integration_method.ipynb: 1000 +rainbow_options_integration_method.qmod: 1000 +randomized_benchmarking.ipynb: 60 +rectangles_packing.qmod: 1800 +rectangles_packing_grid.ipynb: 1800 +RZ.qmod: 10 +RZZ.qmod: 10 +Samyak_Jain_HW3_VQE.ipynb: 30 +second_quantized_hamiltonian.ipynb: 44 +second_quantized_hamiltonian.qmod: 40 +Session1.ipynb: 20 +set_cover.ipynb: 1440 +set_cover.qmod: 1216 +set_partition.ipynb: 152 +set_partition.qmod: 160 +shor.ipynb: 104 +shor.qmod: 88 +shor_modular_exponentiation.ipynb: 300 +shor_modular_exponentiation.qmod: 300 +simon.ipynb: 40 +simon_example.qmod: 30 +simon_shallow_example.qmod: 20 +simple_deutsch_jozsa.qmod: 16 +standard_gates.ipynb: 20 +subtraction_2vars_example.qmod: 10 +subtraction_example.ipynb: 20 +subtraction_float_example.qmod: 10 +superposition.ipynb: 20 +suzuki_trotter.ipynb: 20 +suzuki_trotter.qmod: 10 +SWAP.qmod: 10 +swap_test.ipynb: 40 +swap_test.qmod: 40 +tanh.qmod: 20 +task_scheduling_problem.ipynb: 840 +task_scheduling_problem.qmod: 64 +task_scheduling_problem_large.qmod: 688 +traveling_saleman_problem.qmod: 1088 +traveling_salesman_problem.ipynb: 1068 +U.qmod: 10 +unitary.ipynb: 20 +unitary.qmod: 10 +variational_data_encoding.ipynb: 20 +vqe.ipynb: 20 +vqe_primitive.qmod: 300 +vqe_primitives.qmod: 10 +vqls_with_lcu.ipynb: 1200 +vqls_with_lcu.qmod: 20 +week1_QClass_workshop_with_sol.ipynb: 20 +whats_classiq.ipynb: 400 +whats_classiq.qmod: 1000 +whitebox_fuzzing.ipynb: 720 +whitebox_fuzzing.qmod: 720 +X.qmod: 10 +Yasir_Mansour_HW3_VQE.ipynb: 30 +Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 From f67b06352fbce3b775265b583e616b7c47e7f266 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:32:59 +0200 Subject: [PATCH 10/38] Update test to test for relative paths --- tests/internal/test_notebook_timeouts.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/internal/test_notebook_timeouts.py b/tests/internal/test_notebook_timeouts.py index 70aa64dd5..75a4dd125 100644 --- a/tests/internal/test_notebook_timeouts.py +++ b/tests/internal/test_notebook_timeouts.py @@ -39,7 +39,7 @@ def test_notebook_timeouts(timeouts: dict[str, float]) -> None: relative_path = file_path.relative_to(ROOT) if _can_skip(relative_path): continue - if str(relative_path) not in timeouts: + if relative_path.name not in timeouts: missing_notebooks.append(file_path) assert ( @@ -50,8 +50,7 @@ def test_notebook_timeouts(timeouts: dict[str, float]) -> None: def test_unused_timeouts(timeouts: dict[str, float]) -> None: unused_timeouts: list[str] = [] for notebook in timeouts: - full_notebook_path = ROOT / notebook - if not full_notebook_path.exists(): + if not list(ROOT.rglob(notebook)): unused_timeouts.append(notebook) assert ( From 05052b0aafb1cb3fd124dba6beb534e2cafbd69c Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:37:23 +0200 Subject: [PATCH 11/38] Fix --- tests/internal/test_notebook_unique_name.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/internal/test_notebook_unique_name.py b/tests/internal/test_notebook_unique_name.py index d73e8ab39..6e5f812ed 100644 --- a/tests/internal/test_notebook_unique_name.py +++ b/tests/internal/test_notebook_unique_name.py @@ -1,5 +1,5 @@ from pathlib import Path - +from typing import Iterable ROOT = Path(__file__).parents[2] From dea56bff411999a5c5ebba3ead2b819d8a9f1f04 Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 11:43:46 +0200 Subject: [PATCH 12/38] Update error message --- tests/internal/test_notebook_unique_name.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tests/internal/test_notebook_unique_name.py b/tests/internal/test_notebook_unique_name.py index 6e5f812ed..00934ff47 100644 --- a/tests/internal/test_notebook_unique_name.py +++ b/tests/internal/test_notebook_unique_name.py @@ -1,3 +1,4 @@ +from collections import Counter from pathlib import Path from typing import Iterable @@ -6,14 +7,14 @@ def test_unique_notebook_name(): all_notebooks = ROOT.rglob("*.ipynb") - assert _are_all_base_names_unique(all_notebooks) + assert not duplicate_base_names(all_notebooks) def test_unique_qmod_name(): all_qmods = ROOT.rglob("*.qmod") - assert _are_all_base_names_unique(all_qmods) + assert not duplicate_base_names(all_qmods) -def _are_all_base_names_unique(files: Iterable[Path]) -> bool: +def duplicate_base_names(files: Iterable[Path]) -> bool: base_names = [f.name for f in files] - return len(base_names) == len(set(base_names)) + return [name for name, count in Counter(base_names).items() if count > 1] From 6efd396ec4c573914f2a34a21ca81799303528df Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 12 Dec 2024 12:07:32 +0200 Subject: [PATCH 13/38] Allow functions qmods to have duplicates --- tests/internal/test_notebook_unique_name.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/internal/test_notebook_unique_name.py b/tests/internal/test_notebook_unique_name.py index 00934ff47..5c58620a5 100644 --- a/tests/internal/test_notebook_unique_name.py +++ b/tests/internal/test_notebook_unique_name.py @@ -12,7 +12,9 @@ def test_unique_notebook_name(): def test_unique_qmod_name(): all_qmods = ROOT.rglob("*.qmod") - assert not duplicate_base_names(all_qmods) + # exclude `functions/` + qmods = [path for path in all_qmods if "functions" not in path.parts] + assert not duplicate_base_names(qmods) def duplicate_base_names(files: Iterable[Path]) -> bool: From c43c28b0cfdc023fc255d94c29b7a440b2c5f53c Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Sun, 15 Dec 2024 11:54:55 +0200 Subject: [PATCH 14/38] changed dqi notebook name --- algorithms/dqi/dqi_max_xorsat.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/algorithms/dqi/dqi_max_xorsat.ipynb b/algorithms/dqi/dqi_max_xorsat.ipynb index 28032b698..fa72d8f14 100644 --- a/algorithms/dqi/dqi_max_xorsat.ipynb +++ b/algorithms/dqi/dqi_max_xorsat.ipynb @@ -5,7 +5,7 @@ "id": "9de82aaa-b542-49fa-810c-f008a28a4133", "metadata": {}, "source": [ - "# Optimizing max-XORSAT using the Decoded Quantum Interferometry algorithm" + "# Decoded Quantum Interferometry Algorithm" ] }, { From 4741165c5a0c4b667dc46e20f6b2853bbd9cc039 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Sun, 8 Dec 2024 12:20:11 +0200 Subject: [PATCH 15/38] refactored set_partition, max_clique, ilp, ising --- .../integer_linear_programming.ipynb | 406 ++---- .../integer_linear_programming.qmod | 1093 +---------------- ..._linear_programming.synthesis_options.json | 44 +- .../optimization/max_clique/max_clique.ipynb | 449 +++---- .../optimization/max_clique/max_clique.qmod | 771 +----------- .../max_clique.synthesis_options.json | 44 +- .../set_partition/set_partition.ipynb | 439 ++----- .../set_partition/set_partition.qmod | 732 +---------- .../set_partition.synthesis_options.json | 44 +- .../ising_model/ising_model.ipynb | 312 ++--- .../ising_model/ising_model.qmod | 170 +-- .../ising_model.synthesis_options.json | 44 +- 12 files changed, 804 insertions(+), 3744 deletions(-) diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb index 62a5e07f8..1ee489202 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb @@ -45,28 +45,6 @@ "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve." ] }, - { - "cell_type": "code", - "execution_count": 1, - "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:03.709227Z", - "iopub.status.busy": "2024-05-07T16:03:03.708726Z", - "iopub.status.idle": "2024-05-07T16:03:04.242888Z", - "shell.execute_reply": "2024-05-07T16:03:04.242261Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import networkx as nx\n", - "import numpy as np\n", - "import pyomo.core as pyo\n", - "from IPython.display import Markdown, display\n", - "from matplotlib import pyplot as plt" - ] - }, { "cell_type": "markdown", "id": "8517ef73-faf6-4fd8-9db8-4088551398e0", @@ -79,14 +57,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "48889b21-557b-481c-80c5-3c0b5c91adb6", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:04.245996Z", - "iopub.status.busy": "2024-05-07T16:03:04.245397Z", - "iopub.status.idle": "2024-05-07T16:03:04.250669Z", - "shell.execute_reply": "2024-05-07T16:03:04.250107Z" + "ExecuteTime": { + "end_time": "2024-10-27T14:14:26.846158Z", + "start_time": "2024-10-27T14:14:26.840633Z" }, "tags": [] }, @@ -125,14 +101,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "6e5295f4-7ba6-4ff6-8782-1c4c2c7f85e4", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:04.253099Z", - "iopub.status.busy": "2024-05-07T16:03:04.252586Z", - "iopub.status.idle": "2024-05-07T16:03:04.257612Z", - "shell.execute_reply": "2024-05-07T16:03:04.257011Z" + "ExecuteTime": { + "end_time": "2024-10-27T14:14:27.078661Z", + "start_time": "2024-10-27T14:14:27.071956Z" } }, "outputs": [], @@ -147,17 +121,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "345330b2-9c14-41f6-b4ba-e11fb9ca1565", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:04.259676Z", - "iopub.status.busy": "2024-05-07T16:03:04.259506Z", - "iopub.status.idle": "2024-05-07T16:03:04.264367Z", - "shell.execute_reply": "2024-05-07T16:03:04.263732Z" - }, - "jupyter": { - "outputs_hidden": true + "ExecuteTime": { + "end_time": "2024-10-27T14:14:27.096276Z", + "start_time": "2024-10-27T14:14:27.091814Z" }, "tags": [] }, @@ -210,134 +179,31 @@ "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "816b468f-a59f-4f2f-8337-4a9d66548425", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:04.266876Z", - "iopub.status.busy": "2024-05-07T16:03:04.266379Z", - "iopub.status.idle": "2024-05-07T16:03:06.767843Z", - "shell.execute_reply": "2024-05-07T16:03:06.767070Z" - }, "tags": [] }, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "qaoa_config = QAOAConfig(num_layers=3)" - ] - }, - { - "cell_type": "markdown", - "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:06.773025Z", - "iopub.status.busy": "2024-05-07T16:03:06.771615Z", - "iopub.status.idle": "2024-05-07T16:03:06.776787Z", - "shell.execute_reply": "2024-05-07T16:03:06.776135Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "optimizer_config = OptimizerConfig(max_iteration=90, alpha_cvar=0.7)" - ] - }, - { - "cell_type": "markdown", - "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0", - "metadata": {}, - "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:06.781190Z", - "iopub.status.busy": "2024-05-07T16:03:06.780012Z", - "iopub.status.idle": "2024-05-07T16:03:09.997939Z", - "shell.execute_reply": "2024-05-07T16:03:09.997174Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=ilp_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1fcc3812-c9d0-421c-84bb-38047297b33f", - "metadata": {}, - "source": [ - "We also set the quantum backend we want to execute on:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "53bc041f-065c-44d2-b220-dafd9d0504ac", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:10.003163Z", - "iopub.status.busy": "2024-05-07T16:03:10.001962Z", - "iopub.status.idle": "2024-05-07T16:03:10.044489Z", - "shell.execute_reply": "2024-05-07T16:03:10.043733Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", + "combi = CombinatorialProblem(pyo_model=ilp_model, num_layers=3, penalty_factor=10)\n", "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", - ")" + "qmod = combi.get_model()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "62ec28b3-cb49-411a-8c4a-8004fff6c105", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:10.050654Z", - "iopub.status.busy": "2024-05-07T16:03:10.049516Z", - "iopub.status.idle": "2024-05-07T16:03:10.111490Z", - "shell.execute_reply": "2024-05-07T16:03:10.110833Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "write_qmod(qmod, \"integer_linear_programming\")" @@ -355,15 +221,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:10.116541Z", - "iopub.status.busy": "2024-05-07T16:03:10.115462Z", - "iopub.status.idle": "2024-05-07T16:03:15.845871Z", - "shell.execute_reply": "2024-05-07T16:03:15.845158Z" - }, "pycharm": { "name": "#%%\n" }, @@ -374,39 +234,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://platform.classiq.io/circuit/183bcd6c-83c8-4610-828f-d7cd40f9fb85?version=0.41.0.dev39%2B79c8fd0855\n" + "Opening: https://nightly.platform.classiq.io/circuit/ae0c345f-e27c-496d-a3d8-a7eab1675d72?version=0.61.0.dev7\n" ] } ], "source": [ - "qprog = synthesize(qmod)\n", + "qprog = combi.get_qprog()\n", "show(qprog)" ] }, { "cell_type": "markdown", - "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304", + "id": "b119464b-9d46-4ea0-ba4a-1734f3e0e3e5", "metadata": {}, "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" + "We also set the quantum backend we want to execute on:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, + "id": "7d188c69-21d1-4afe-86b1-46229e91a01e", + "metadata": {}, + "outputs": [], + "source": [ + "from classiq.execution import *\n", + "\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "07621d7c-0e54-4adb-9c47-8fd99a346e29", + "metadata": {}, + "source": [ + "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:15.848412Z", - "iopub.status.busy": "2024-05-07T16:03:15.848000Z", - "iopub.status.idle": "2024-05-07T16:03:33.645387Z", - "shell.execute_reply": "2024-05-07T16:03:33.644679Z" - }, "tags": [] }, "outputs": [], "source": [ - "result = execute(qprog).result_value()" + "cost_values = []\n", + "optimized_params = combi.optimize(\n", + " execution_preferences, maxiter=90, cost_trace=cost_values, quantile=0.7\n", + ")" ] }, { @@ -419,33 +298,41 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "02454398-b229-403c-824a-b1eb539fbc1f", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:33.648272Z", - "iopub.status.busy": "2024-05-07T16:03:33.647738Z", - "iopub.status.idle": "2024-05-07T16:03:33.672693Z", - "shell.execute_reply": "2024-05-07T16:03:33.672075Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/jpeg": 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2GGsvMjC/QeuOGplscIxu/uVf/qXOS+gUUjgZUNdT9h//8R+BT+inFPpqNPwUoVrqQcJGs006xe+44449e/Yw9aE25RKvA5Dm0ac+9SlgHg0BGmleCvMYXiWM+vznP083yUxtWsReLQdWJwciU4jVSZylynLgbDkA2oFAjO8UREjiFiUw2mPGa03h6aFDh0hkvEagRMYlm0KgDvFcAQygAmz4iZ/4CUoBDIABEeCKK3jDleLUyQou+VXIRpIG9YEfqgX5SKQGrIqAGfITyAnO/cf/+B9R3nKLCEiFoD7xBCpoC3CFHkAFIGcCQSUE8lAt2agZ+kmnZi1IOrhOZmyvgB8EU/pIv2gOHCI/GUA1JYOqwGa6zzouWmtqQJRH044gTtMo6gFaApMVsBY2wkCIoSy3CL4UJ04EFCSdq+r2wUiqIsBMWmehFzKgliszFQKNkpn+QhLp1Eyi2m2RAo5Slo7DeTQTADC4ri8Rcf8d73gHT+E2PYLDatdNCgv24C4dpAYYAiorwfCBCUdSIREbLAdWJwcsAK/O92KpepEc0FFbR3lFTUQuxm6QgEGZQR+EAH6oXTEP1NGWgBYFacZxEkFZCv7Ij/wIRtQoZhnfqVMh5BWveAXIQWYqJCcRUIp2FeGoH2zGbkvrp3Lq4Ur94Me/+Bf/ggVjTJZYGAaAEfsQT3makKHEvPQrPYUwglYFeTpv0DkEYvdv/dZvAXWAJQrh7/me74EAhUwoZ90XOZ6OAOoKwGxlZuWVqrCUJhsbn3TKAt4zGyCd3nF9nl7QWbATnMb2DWbCHHCU4jo5oE4aAn2pBIbQNIE4RWBaUi3d0YYoRX5qQBZnaRkZnQkT1DLpYfaAPK2vWyukHhssB1YnBywAr873Yql6kRxggGZwV+2lgrFWpFDE0K+3CqXEGaMBIWQvRnwVrcBRBneggjzgDcADDPCUdJAAeYunIAc4AdCCylRCQ0TUWJoFSBCaRMUALHhBBeiBKnCLgAIW8OMWGVdFYSWJK8QTIAltLVIgCmpmAwpXPKVp0AjsYQJBOkpabJfITzryIogLtSpTQi1opLTpFZppkUq48hTExeiMRWImB+jPWeXlEf1FkuYp/aXXPKJOek39PIVFXJWNpNMjsjEFIQDGSP88JcBMSEJ9TReI8BRKCCyNwwc4hihMNuihL9BGd4ggMdMQdUKM9kLfBSmgKeZyMJNS1KYvC5Igj5wIwTpXIJv2FPYKHSbQF/LHd/av5cCq44BdA151r8QS9FI4oDDDsE4ljPIM3MQVpQAMUkgHBhi+wUviQBdPwTwd8UkBYwBajLP+2T/7Z2xxYR0UbABjGMoV6hSQjhw5Qmb0wIz7DPRIliifaeITn/iEtg5aYKJMCk/JCZZwJWDAzLLrn/zJn1D21ltvVdjQR1BC0xSnuX/5L/8l1GL0y5VEkJU8RGgIuRmCsSgmRfuL7TH1INcyM6A5JgS0juBOZsimR9pfpYREDKGBWIyYWIhl3RTDYxgFnlHbd3/3dyOtPv3001QOcNJZqIIJVEIXiNC0UgIME6E4GmwWelH8cguKsxYOLpKZIoDr933f9wHe/+W//BeeahH6wlP6yFVFc7LxiN5BA9loQvtLL1j9BYBZsSZOfojhddApIpDK66MXFKeUzi2IKPYTsegLE2xYzRywEvBqfjuWtrPmABijUEFJ0IjrPffcg5qUsZtbBEcG5Te+8Y1AC2pVRnxEUsUwRnzijPIsdurAzabShx566LLLLsPeGFmT/HfeeScIhPEUtZGH/AAV0htx2gU22CeD3TLLulhQg39st0VaBfBoCIAEWcEzHtEKBkSgL/ADeJCBCrlCLYANwACHSHJsQ6JCJF0IplMUwej6J3/yJ0FuFOOYXKFVxqvXww8/DJyzoIsmVgkDs8EzyKNCCIM8KoQAIklD2GazGs3eWdAXeCYnkEapf//v/z12TzxFW45YiZqaPmKizKyFPIjjoCmVcIu4TH569L/+1/9Cow5t0EBD0AwlIDFxegrxpP/n//yfYSacgfMwkAqxL4OxkAp+kxPaqJ9b6IRdNAQrSPz1X/91MqMVx9BMXyiL1tTGuj5TGYyrqQRzOSRs3iOsYEqBUp3dZfCWCm2wHFjVHOBXZ4PlwHriACM43WH4Zu+sSlQM+vwIgQod5bFDJgNSGgM6eUDKpPtkA5aSWzADQ2h0p6Qj6gED2C7xFGnvL/7iL6gcm2HNrJIucVS77KuhCM49eAqSIcORDtCqLE5ORDcqZDVUEzVd5TZyAkJaJ0L2f/2v/5V6gBaQjG3EKKUBP54yG0DqRU1Nj8B49hyDhWAtjwBIdtlCKincwg2wEKFZ9w6RAgFckVMpC0qxIK0EJO0+/vjjP/VTP6Ur5Vxf+9rX/v7v/z5FyIAkiuYAvOcW1ORKoBeIofQU4AT5EKDZoMzERRtSquAz9QCrBPZE/dIv/RKzIi1LL7BkphJTmaivMcPG8IrZkqZg58XmKEoxR6F1zL+ZBukj5gHswGajMB1hGgEMI2pjmaVPkzr11l4tB1YbB8SpOmOBDZYD64MDYAmBkRqRUXvENxwAA94AYGAMFGGwJg+3QAjZiIBSxBnBAS3iZKCsorX+YoEWUgA/ilOEwC0QoijFQE+K4o2WZS1TYRvAYIUVGZH8ZEC6pcLXve51NPS5z30OIZKaE2Igj2zckkeb4LYzQJsSoLSpJIryXMVccpJChUnrSbo2nVRFPSpwK3O0FHlglNac5OwsCIugkKVrIBNQh12aQvcpSFvoALQv2GCjw0fTziOq4soj1MVcCdoc6VSu3VT2Jo2SgVkRrKMgdPJUyeDKLcTDRvLQKEG7QFnSaZ1HWg+3PF1Rc9KEjVgOrAYO2DXg1fAWLA3njAOgKRDIsI4gqKM/ozA4wYojiYCEAiQRRnPghPzEGdaBE2QvZGU0t4CQ4hCjPNkY4pU+bsEPRnm9BX01TnHENYZ+2gJmeKroi5RMOsIo2QhAAo8wy8JSF9MhBGjyk6L1QwlxsnEFNkgkDtwStCCtkw6F0KbpFFetrEIUBekpgdbJrPnpna5z85RSqkmmZrJpc0xNFKWgn5p5RJe5UpDMCZ4pMykC+pIOu4grYTCWmnX2QFnkfjjJBmsy8IgA04jzCjRCcxRU4CSutzCKV0Y6TZMC+lKEOH2kaSUDFhHIQyCPPmIqQE6oJTMvgjhB4+TRW3u1HFidHLAS8Op8L5aqF8kBkIBRmEFfyzNS66BMOolcATAd+smgERAIbOCWUVsFL6CLuEI1eVQO03rAHl1bJQ+1kYen5NHMuIDAbkude7AS+bd/+7e4pEDHS/1UyJItPjT+4R/+AdkXNS9KWhJBFL0qXia0JeRpR7jSFyBHQQWcozsahySAjXqSnFQIbYqRSaLKrMktEUCLq041qFBBLuGY5oQqcJFHylJWyoF8CqIWZu2WXtMQW3VRWb/qVa9C9Y1eHSU8j1hIRg6mEtT1uhBA1yjIFU5ypUK6QwbU6cBtJ/3KBGUvGeAwZJCBiPKZRAjmmswPiHcGekEGSq1gQmceG7ccuPAc4Ltug+XAeuIAI68uf9Ip4oQVvQOKkP+SPDzlluGeCNDFFUhbUYRb6gGeFW47y+rarbaCrMnOVHYogRNohjk2QKVPkABIYP2YHzxqZ0yHSKFObZRHJzfHI5APeshJo0pYZzbAjAykgOuaDpIpGYpz9IKgBGsGbY4F1BXtorLWDLRCfiohaKNKpz6lWm5XUMItBOB1i/kEvQPwWMHVNVro0YKnvEIDtYHo+pQ470UJ0xQYSwdhtdKgHYGfKwggM9IzV2gmP1fNr5XYq+XAauaAlYAv/BzIUnDOOcBPTqWrzprBOQAJIyCGcsQvxnEkSMZr1Z2CLiQydhPRFDLwlBoQszrlM1LIw1OVHZHnEkkOyY9WSFG0oDaQGNgGjCGJUtSj8p8WIYVKlFq9QiFkkEi2TuKTOBgD2YkgCD6RmSJJhiSilXMLvtJrFZeTp9BGWbpGQwQIJqVToKQ4RVZ0nOIqBNN9HgG9ia6Y26RFsoG+cANAhYeqJyeRDHSzsxWlh6ZBX1UtkEKppIM8UsrhDNoLSNUMyjcq1Jel9SS1Uepkyjvz2LjlwGrggAXg1fAWLA3njAOACsO0VgcWJpHOcV+hjkc60Gs2RvYEAkGCJI/WwEAPRBEHeBI4IQ8pQKmiUVIEqGD01/VOMiC0IfVqWaQ6sJB4ErQst4rcmk5V3HIlAD8E8AncAnU6JxYI7go/gBw5AS2AhwqpREDVYFWShxrIYKoMqScBS8omiEijWj/ZtDhVkUFxjkckMgOga3pVarUGpV8hGRqoIZkWdAKqFoFO8sNSqmKCAsPpI4kEWKd9VwRVtpOufadaaqB1fct0StcUoCFBYvIoE6gz6YW2a6+WA6uKAxaAV9XrsMScGw4oGFAXAzejsFaqwzojsgzzZt8tj8AnRnYSFQj1qjIr+Qk8InSSRSKIwrivGKZ5EhhLAA+QAEJoCNgjEVRIIDlplGqpBKAC20AaIhBAEQoq2cR5ROgkgESC4hPpNE1mytJEgkyk69o2iVo8QUFAS1dGlX6FK2pY0U1qoBXogT/aOpUTp10lVa/aX5hAHq2B+Qf1UyGTIdITuVYr4QrBUKsdTBKJUCGVd3ZWX0dnFygFDQm3V2SgOeVMZyWdTdi45cCq4sBaAmB+/zqE6a9Of//KTVWLkYG5PKOAjolkIN75O9ciOgBRCWOHWt/wo+3MtqrekCXGcsBywHLAcuBMOLBipnsmRS5snrUEwCtgUufvsI8Zd6IzZGpMNlVGKU6TgZxcdSKPeQgwTH4UicAzmREUQF+dwl/Yl2FbtxywHLAcsBw4mQNoQVTlw0BNXOUl4tjf4c0NmQrpC5sDLbgCKU6ubfWkrBkATnRNoKkuWRGB0fjowcsBsIoLHnwY4aDulLgLx1HB4WWQt4UnP5wJ8BaT14BgrSCdpNiI5YDlgOWA5cAq4QBDPfISKwsM75AEADOAA8Bseeeob3ymKp26ILJKaD4TMtYMACeTmmT5B0EWJ7EcGPfnf/7nvIb//b//N37w77rrrl27dqmSWYvwksgJZvPyQFly4oQPZ4FIyQReGMtRrPlZFfSZfF1sHssBywHLgZefA8n4T4TWGa6JMLBv3Ljx8OHDjOdKkkpfCUa8/HSebYtrBoDpWCLaKn+5ZfchrOdMN5ZygVLcyuNg6Od//ucT+xRKMVHiVam2GQc9vDBeGxkQiHmLoDIADEiT4Wx5Z/NbDlgOWA5YDlwoDiBQ4fGUXQaM8KigE3O/NQTAy95zLhQTz7xdBU7y6yQI1MQ1PO7psUcljjiL5x0kYEXfBK0pxSovT0nXR6RQCejLqrAqoq3N5Jm/BZvTcsBywHLgZeYAA7WO0gz+qq1U1SZrwOx8Y5ufoi9jO9mSbXUvM5Evorm15Cs1EVLhL/iqq7a8D2RZfcTiLi8DLvBuFGs5TA3HeJwBDkhTCvGXPSG6YYMrOXlbvDN9oy+CfbaI5YDlgOWA5cD55kAiIyURbRHcRbgiDiJwZVRX8ex803Ou6l9LAIzAqlwGLwFdABiUxX5K5zuooIFhJkoEIirm4hTwZ37mZ/BVi4KCIvv37+eKgoKCZDtXTLT1WA5YDpySA/zGVnxOzMZ6nn4kmZzsJk7yn5hT0qOQ5DiDSJIlLhy3GMYR8+CkbHH2jr9Jns4IBCefzvS4XNKKRgCJjpTOAp3xjjydyeckHhPGX6WkI+EMomdLw0lVdna/5YQnfhxuTypx+gTGcLbAMLaThSFd0UGvpy+0up6sGRU08xrmPnAZ3AWJ2emvHOfM1Fe84hU8ZR6k/hN0ipSs7JIOQquMq2oKMhNUaCaiovDqei2WGsuBtcoBRlgNHmMpN50jKv5EmPIbryImGyiojz3+eopk6nMEiwzJqU9dyUWBKKWzRpqKbrVCT2uPSZBSVEtBKnS5S1qkFC2Zjzat9UfZTPm4HUpLMA1oNLqSgcq1JpKoTLNqRJqTT2goNKVdrpqFDpOLh9Lz5SD0GCKl55KZ+oOoyHKus40lVFGQuGmYyg1JjrymMw9a5szzdzYtLcq8xzBEu6kVKQURcyihhVY2gl6TQZvhnUGb8Zw4ETKh1NRHIAIpibubleVX5f2pu7oKSYXjcFkJY+LDNIdbNh2xBkyiToU4gIWDx4FhZNxV2AVLkuXARcEBGWTPIGg2Bl/9xICQ4IFgkN7ESSvr1XSDJ2bU7kBfTQRalyHHkEQ6iMPHN9eo7DJ2GnzQHFp0eYRM7pMIpTs/pOutiVCTqUy6mXyk4ZaTDp204yL8kDHuftJZEt0UGcjWkmkD98GZf3ynZT5SMqGTSrSjMWEYwTBC1swV2s57iF8gXTMkiGc36abcyoeI+Thczzsxq6qBNSMBwzVVHeskiFuWdT/4wQ9+z/d8zytf+crLL7/8ox/9KLuBOeocx/erisWWGMuBi4sDMsgSAhdLx5N6bgZYM+iTDXDSAVeuwIynaEAp8HEZfd0oF2ltclKvqVYQWovHA/xya6SbRMQqGeNMKebvKrAm2UgGqORDZpO/7TmgU0I2GaK2IhClaNxYGPguBf2IBqk0fiTRFcAm1QCrFcdDDQ1JWKBkhMiW47ZNZnCILNSWwm0QNLQM9zhI2Ze7MwzUzTHPkEGfsk6YFfI7iJJaaE7yAMANgT0n5QotkAfBL3zlHZ1hzphzUm9HMIgb30ekScej5qHb3MU51vvftQTAumqrV7QNvBoOPsMLx4//+I8jELMXm4NXOZGUVV7Wg1e4vF/v79H2z3JgFXIADH7+sAIewGAZf7lGFx2hO2rRBDBE0pL0JNLZGujIrRbgKnpsFW0jSDCtrIQnimh6UpOpOwYRqc0kAP4ygeAW+EzymojOP+SqZC3X13Y8kpIH9AJPuaa45uFhikSCZoMY0wWtxzx4gYuRJsFuGpEZgFa2TIDj6iI02RR0k5q1gy98NXp1iHjhnHEeT7pg3oOWgVtElGca16dcoYYpg+aXMhdBWEv7gLGrYr0WfEUIJujbQTUN3LLQq09BYpYB1OukLgyQTdcMiLBijwdKrqwTaA3Jo4vgXdsuWg68bBzQwZaBNo4kLSs+ya3BHhmJZZUzHosjDI4GaVLJb+AI9IrzJHWdOpIADmjhOXWuocN5Er4upkqDBKlZKiag4EXmjJZso0VZrcM0rbmEbEnspIF7UyqaD8QZIjlec3LlQ6ANhIYM4qxIvSiZfXqkeKgZTC8ljy/KA3AdiyTuZLPGmQazWB73iwYhjBa46luAXoZNqZMkGtUWXclz3kJk62o47coECFJomqt+NK4cgDgAGPpOGdbleH66zp6SAxcyEWRVqRfgBHQJUIM0rOiLZRavR82pQF8F4wtJrm3bcuBi5gAwtmxlo6OrsgOIQO0sP96OYOBXbHAUfU3+qNDK/CtKUuZ5QzTIgzFGYpWGlouABhQWaNBsBqgADGlac/GcjnCFDAFjnvBAr2Qi4otQS5rUpPUlGaSaOLPG5b4NrDaBIdeLdL+KhTyJ4RD0bTpBywkMPR6rxaaWM7hSoGWIhJq0EGaU20i90TSIFVZ07b7Rbwssky2WOIlqL57vGvf0+fKcWI+ZRmjNQj9dRm/P5EP6pF02PJQcBABY083dRXFZMwDM20jMm5M3Q4puAgOP1fCKE9CQcTVnks1GLAcsB14eDijSnHh84ilbFsRahpaTx13Bb3KoDKrjs2RHOlQ4Mg2JerNTrjXVRPBAA3IrQqQESKII4CZXk5JgjikFNvPRRsltyJPWzUODr6aQqUrxVusXasis+eWpVgthhkJpixSNk2h0v5INKnynSgoQmNCjjZkSCvyGeC18JleBXPmY8yPpC3iXcYKUAWBToee7vigSkHlZaaZ1PmZJXel/4atZA5YiVPKCV0OLwWD4CXvpqEQ4c5oX5wo3OvgmdxKUBo2v/+uaAWAVf0FZ9hShNyYC9KKORvZlcxFP8UaJlKzHC2rm9f/2bA8tB1YvBxhJGfQVXFZQKYOsGcEjECMOjoJkbYElRnZGaj9CLS3Ks6gq8AVowYkslRDXcZy8UqcR0fhLozGoKQALsiEcNg1+dzyVUnyAZUyWTEtSjSScMpBsYGP5oSmkZSRRCejIs6L7MEQtfkmH+EgDbXTglDaUyDXlgFAyITgtJcsUdMRoNyV4qzyMH8AhZRIc4wwDUyU5AWm6w0dZFyOf9uUFrtrrF7zKKrwsNqPrjyuMItBmSsMEkc55oYZRPJX8awaVYha/+L9rrKvqcwN5VwVfdoARkt5b3E1YYSOWAxeEAwzozI9bgcNKEdKjiqdKCSux7NtMobQ19+SUPf3pTNBGLeoirZWbrVw6xUjcqDVLuazkC5xmedHLeH4222jUsxnkuXiw5pmbajfq6HK9dJra2kzAxTmP56czCiz1esvNZwA6ln9FV9xcdJqLHnmjYQ8sRMGLH6U8C8UC7PGqMKRi4Uz7VCuB4qSADnrDPQFlNao2N4WGF5whge426tU0HU95TqMp9sXATEjFPFPNdib0C4iesMWIj4CNqrxpEAbQgtckIXAyst/VZGLPK6RFgrjUdEIwG2GXjc1Z65bWqMygWgobbZqXzcQxBcAzSm2x9RIzMl+sypn0EFJexBQK1+q1fBa2nBBaAfxt5eHtqYK3jKwsuQcsDvqeLHKHbjZwEfKXA5QxlYJDTDtcqMODlUxYQGLIhDSjcl/Ovs5jawyA1/nbsN2zHFjjHEApxZyY0ZjRlHVXhaxaG0SROCADKNWbYV4Q0wF9BSwMkpWb7VRaduBUamFXLkukXm135fx0qctpcecI+oZBe6nsZzMyfstA7viZvEpXlXK9UMz6bI6g0TBoVuvpVDqbyywKHIkwyT8/UxD8D+tOo+qkjMjbdtpe0E4LDunqaa3u5KheqJMWormC6Q49AiOQvFO6u8oYL0mi2dnD2i7Qm84WWWGVvmYyQbOWAlIxFw3B/QVw2SsV2f1DBmomQuWULTvusfH5fLbQ25suUdB1KaFNQzkuhMhokFxYpxzVq5hOGdDWdO5q9ZbXSmXTsEX4TzYmB7Wg3hQXFmkqzrgKxk4TtmMNFjr0XZQLRjJuBE657CwtlXmJDz10F1Y1vBpMaljUw9ky58ix8zPHtEd4szLMzVXIjPENgWdYufb1FUu5ZR6SCEmUBWP5epCJCUYWZqbhD2vekAKxLdWer6x9/d5bAF6/79b2zHLgZedALicHnOC1jtG24Tr1toE83zk44zz04AMLs1Pve89bcmnRJuNMB2k1QekWYqTjHJmuf/HLt1+z96rrL9/UYMGy4ZTELjbdblT9LPJawy92mT4JnoKrAiFIyYGTLWZpEfikllzWSxeKZGsIthiXExwi7qXyfjoVpp1WSWYCyJT8kToEJAxWYflUH8zSDGjgVRvNesNI8tmMyIwGP2pGxSyExiBKwUrdmZl2xo8fm5uZSqe87Vu27NjWwwwjm86DN8xCsjTVnRfy2oYhGWex5Tyxf/H+x5586sCRY5PTs0sLSINbN2285orLrt6zc8+20nAuktIXTbs0R0M6G9ArNJACknHlI7jecKpB6huPHh8a3rB9kzOccdANZhHwvVzRlIGAuYbz8DOTX7n3oQeeeGa+2vRyBZkpZPOgLG8Ng1aW9sBdmKMbSZCMuSUQgQZUj2n/1JChVrGan6pQVbIy2FPIXLl9ZNOGwR07toyMOBmPQ9mdCmqIeiPrthtLc8N9XRsGS2HDxTVJMR+/WeHuxRJOzc2Lpfe2n5YDlgPnmgPIQEv1drorDzCAhxN1Z2rG+cQnPnX/ffeEjcob3vimzQPIoebEFARE3Fl4uHwSZTBS0f2P77v97gfveXjfr//CTwyVnEbdqQOuniuirdMGHjLpVLPlpDIZNyUYjyTHLQIf0EKWlBG5qrRrdMhALNAJbrMxEWLw/HRsOkhlvP5ekeJUkiMdYshWZOkU9TUQWa9WG0GO41syeQiDqqPTtU9+/su1tldru60wFfqZwPVpGmQ6cOAAp48HLQGtanmxXqtuHB3Zs3vnm97w+h3bcj1IoqjWzarmUsM5fKT8yCOPPfzo408/u7/BQajZEufAZPKljSNbmElMT89+4lOfvf3z6W1bRvds37pheDibzx0YG5+emz127BiH7qnhC46GcMSLVIqrA65wmyNn8Mg7PT291AjdQu9SpZrzvMt3bH3tTTfeuHfjcK90cKns3HP//i9/7a6nDh0G7zLd3V6x2Gi18Zng11pp30faBabT2Wx/f1euULhk+3aYnE2nkZ7Li4szc3OL8/MQgJ9BWHdyyORyrACyD4VdKrAFLJ+enRsfKx898Ezaa8EcsBkNNjMm3jlznFZ1sa+Ue+e3vfkdb3lVMYd+RHTnzUY9lzGTnZMbWKcpFoDX6Yu13bIcuAAckAXaTCbVlcmIDplx33G+cNfBz37hS8BDyi/l8+nx2aWuXO9AEZUysi1YKpJopc5aoQtAHhyb87uGnzl89BNfeuj733VNNuss1UVbbBxWyDHeQSYfZsSTUwNpMpBVW98sGh5dFAAu5ERiBm8QfMkQVsINSKGeX207jxyc/MzdD9750FPTTafQ04d8tmPzxhsv3XHV9oFt3SIsZgDRdsMpZPxsMZ+V9ctqs/34vgO333n33d96pExn0gUnW2h76Wbgt0I38GSV2wtAr9TA0NCmTZuAw4WFuQPPHvjyPd/6xree2Lp1680333z11SOc0Pa1r939xBNPgEBIh3NzM6Vi702XXXrZrm0bhwY2jQxu3JBGfn3uwMwDDz/2+L5nx8enDh0Za7NC63uspvIaKRViTeanFhvO0uSiM7nYbh+BG8idoB0YDMLl84WBnswV27cvTM8enpx54vF9Dz31XGlgqG/D5p7e3ieffHJpfi4VtHZfcvktN15/01VbRnpFdQ4c4uG31XRabSedcnJ5pyRKaTEPA4/hJPHA2aC3OmU55ddK5fIkA7OW6Rlnen7pgcf3jU/PHD58dGJyErVIvpDv7uoq5dLtem8x5/f29/H+aUI/gD3fiaSSUza0zhItAK+zF2q7YzlwQTmArjLe6HLfY9OfuePeR585UmmGr7v1HUuzkwee/Nb0QmXP9l4hET10HbnVGEzJKqeMwgfHJmthtuEXPv6FO6++9pprt6FPlnR5GLQxxZqqhz7qS8cZX3KOTSwcGZ966LGnkCnbrPs2m6VC8fI9u1/zqldee/VAEamqy51vOccOLtx+zzdvv+/+o0v1TN9QLpedmptHfp2YGLvnjq+kG5XRfOayzaPXXrpzy9aNA1u2DAyJndFTB2e/8IUvAIjVWiuT63rn227LFYq5QslL+a0gFLsxTL9S3rbR4ZFhp4BIZ8RcFLWHpl7zzQee/sodXz168Nm/3v/Mp3v7U5ksUjLq7K5i9jWvuPHqvZdet5OFZrPcGTgFY9sVZJ2Nl/XfdNnrJhdet+/w9L5DR8YhshUMD7H2OrBp00ZUuDn2LbWc2VmWaRucg0tAHgWDR0ZGtm3btmGD0+85JQOcs03nvsdnvnj3N7+177n9+x5LF4vlpaVX3XDtba999dWXeEOeU0BRbwgGOIHelgt1ITMJtP54yqAXLA7Adt7KiiucEWX0aQJsEdsx87S/z9lYLF6z4zrmVUyV0JYDNlRIcT55JmcNp5iWRMywmq1GVy5TyucuKvSFT2vJE9ZpXvppk5kbrnB3ZT1hnZZZ9oHlwDngAMM4dkr+TNW5+7Fn/uFz33hw3+FtV9xwy5vfetMN3h23T/2/P/tfH/iut/3g264FfnIBw37N8TJokDFCRk+74Dj/8lf/99hM5dLL9j754MO33vyKH3jXq3cOymCdDhpue8lJF2ed7BPj7S995Wv3P/LY3GIF6bjtZZrtEN0pASyrlhfq1Up/X88Vl+3ZsXPXw088e/Dg4WMHD/aUsm+4+dW3vuGVm0dFs/3QQ0empmf28+To2OTcUq0ZsOzpZvLllrd565ZSMX/s0HMTRw9tHu6/9fWvfs0NezcPOQVXcAvAQC4UIVs+QbuJITeWSU6t0Ua8zWbFihsw5vPNhw5++vNfevyJp/qHR197yxuuu+G64X6nOy2VULZWE4Nk5HXi1TJH+ohNMmVRWXNdrAVhyssh9Ha8FNIJiogApjkAV0zGqEFDo+F0YSeOWp4ZCruRHGe25uw/ePTwsaM3XX9Dqeh3p0TZzkeqbTWbjVraHKYbVxD/xZyK1VqstoB3TMLU6Kzd5gCcFBZwpwyuGxolMw+lkCwZSBSP1pABzVDEVeNEoBmsJRORtJhDK89021LSIVNHfFmX47myKe6i/Ws5YDlwITlg9tiIUi7yCQwtjJVmTCYqnguTgYxRiuGMWyKKCon0QIocVGDGOR31TArZPcZ3UsjPFa0qwSj9xOyIuKlBaeBWmkOTTAACpULxJEwQS2Px8WBuBDVMLFR73XYzTKUBt3/81BcfeebY699029ve8XrQi9JbRvu9fM8zR6ZRCOcZacmfzmL7GoKH2SyyXSOF2VSqkEr94Hvf+IdTx+974O69u7dtvmUjZXNexvf66exUw/nUF7/y5S/fzqLjtm07BoeHdl962dVXbx0pSXeOjjsPP/zUAw98kzXRb9499cgjj8zWgnyx69u+/bZ33nr9rn7pLCzLZ53tr9jcdjY3nKuRz47XnMf3Nx94/KlnDx0LphaOHDnWrleG+rve867veP1rLt81JN0XHbWBLuPYAg2rcVbVbqVYf65V2KRUZN8OUl7QrjeDbFZMrl53zbabr/lhGoQwNiWhKqcSCGCxG4VyEXSlY7wI6AF92aRcD1vtJlZsrh/kcmIWLFw3vEXDTE6uFJQyxNEZxFComm3WX4sQh9SBCRUCrAnYIW/Zs8HhE2J3xusLwwb6arOnygvS0BDQF4PyNEHl+oHIZHtn3CLm3ClZu6dtrfvEa7uF9XY6WcFlb5keIKu7nYwTSqZmSMJ0iD5AH9VI95hagfFkY06h1ucnVry+7ywAr+/3a3u3pjjAeBTWwnqrnOrBbR/jFAMeo7bbrKH5nJsvuz39+CxmQGfURI7hWnWcJ55ZvHFb1wCbT7itNwrZjOAxo7cZq9lGSjbxzyCuDb0K5sNmH+xcpfXJB4+++tXbtqNsrFbcfImRMRO2W5WFFIAQpirIdIXcjMH4rcwIeOxjVsw4nQIcWHzVFlhDxMAGY2LOaGWAz6bSNDfXzOyfWLps71U/8UOv7zPQxaPjmYpX7N03sYgNDsRDDMeKguNuGjW0U0g5T1WcuaXKjlxq77Dzz7/zTX/4v/74Hz72iVe8+v9XSEt+eg16ffRv7rjjq3duGBj8sR/6gZsuH5R6oI2VYPOed4w4r75tT+O2Pfv2z97/rYf27dt39ZWXXX3FFbt2wJ44p2wLEgDgSoUlx+nLOXsuT7/78r2Bs3ex7jz77LHFxcWtWzbt2Ii9tNTceZUNTUIOcqFOWqiF5yYAwC5zCXF2QSYlSVsJpPkoEWINnkbbiuVEIgInJeUwRFNQpXQEczJN6QimYMe9iaaEf1FgYTqOyt+YMo3xCrF6NnQpsHN/Yv1S5sUFQLQziC678x5KMHCLeAId0UPmEcxgJPhiLi/hxGImaR1fLACv45dru7YmOeBms+zZBFnr7IpkQMebQjq9uLBY6O1fRDnpOM+MOceOzz94710Hjx45NDe/YWjozv7ef/Wj7+rLOplsphmEGTCWgd+E+C9DLzApAzHAyQabL33trr/7+pH909WffsdlpVwOSdfkDFOIPozOwGw2V3Gc//PpZ7K+96HbLukCidttxlVqVfE3rplqgVdx8Gs+onKsBH7TS7PHk/EFjWtW5gD+cMkPvDRmRPUmNs9szxHXxI7XQAZ02Y7qO/NsiXX9wUK2x3Nef83o16/cfd+DR37zv33y3/zCO8pmYP7w733unju/cvOrrn/3t9963eXgprQqy4ZsJMV+SghCqhbT5Wt39F214w3t8A212fnh/h5lRbMlZlzIkVjkoiYl8eThDx6O7BxoNrtx9QMgIFxKtkihqtVwpWwHTiyjXJRBExRPkjIdkY6ySerKSk6VJ8n8IiMdda6AxxdZ4dkVS7p4ImcSqpLI2VW7pnOf/A1c092xxFsOrGUOuB4+J/CkwAiFDlo2Z4hshyjMymF/zXG+dPeB//PX/1Ds6T+8/5kN/SW0jj3tZntp7t5j+79wzyXvvuWqWtPpQ74CAxnt+BgpC+lLxjbQzhjI8NDPpJ49dJwdqN986LGlt1wm6lAU2mCNCM58yIyVrzNVcb58+x1IyO/Yu23vRr9RD/Mqr5ihlAv6Z4mK5HfC6ImLKx5k0+z8FCpEsek6AwP5NDt7a5WFcjDUFSIQiWaVzS/MCQJpdWpK0G5osJ/u10Png//yfT/1M384MzH21XuO3XDDxo/+6e3PPPHI5bu2vfMtb7z+8j7kJky4cM4h1WPAHEmBrlgFB27bQ7QS7hVj9CUd/S2BRhV9peSpgllNRjaWAFTrES96a6+WA+eWAxaAzy0/bW2WAy+eA+g0w0wJFML/QVb2t4io0ArcWuhjsXNo1vnTv/rbWrPV1aq+7c2v29Cb37P70t6BDQ8+te/PP/F3X7znrrfcchVmSLhdFFkQ6BNMBEaBQmPSCqYbAEb2JMJW3d6hDYiqS22nCy9OmOcA9CLLBm1cNJRKyMQPPTkJsi0tLHz9gYd2b7o+oJipVHsoswSWf6UtQDQCfWkSA9dqA2cceXbzaFauYTuf8or53GSF7TCLQVeRNIFncFL9Y2HYPD7OzcjQEKWKrjMx03j329/6+S/c/tl/+n9H91/55MPf7Ctkf+qDP7x7s+jY8bnRYyyYAG9U4inBYERqbIZYLpb1RD5KGlIsOIo9JlCaYHBC14oIUwdyAtLkJKL+KFbksbeWA+eKA/IbtcFywHJgNXAA6ALjkAW9sIn7AieoOe1mrRnWPWfOcf7kbz6FY6lXvuLG3/73H/zAe7/t+9/1+uv3bLh82HnTTbv7hweeGTv2N59/ALFSlnrVdEqMbrAvxlyILbF45ZWaDaRL5MDx6YWGM71Yf26s3jD2t3gOFosgloEDVLqy5vrg488FYTrf1fPV+x9EJe5nusyBgcIqBg6pCugzKAf68ZdgzHhwJykbT2Q7rUlEAmaPEEUGxAVGMDk9g2cNk9+IziKSSoXj42PoyUeGBpgGALGbBzK3vX6n1641y7Of+/g/sD/1J/+/H9q9MaOa516DvlQPZKZJS2WxLGIxGoqoCiOknCeulwjIu+BoAquIwkrUKa+akyIinceBJuKo/Ws5cC45YL6h57JCW5flgOXAi+cAGAxsplhqZbMr+mhkVna2uM7ff/Khh57YVyrk3/vOt4BAw6yssryK5UrVGc47t936xlqr/cW77jq6YM7fi7ADjANsjPGpEUwpAiJmU9LEfCOcr9QWqq17H3sW1JeNK+TEJhbL3kxOMoTOM4fHp6Zm8qWuZ8cnl6AKn8kNF4Qj0ILYQQviAbqRebQ0C84jAdeabdfLG3OwZIjhwebRYZa3j4yN0xo7cemsVOOmTUQkYAB5ZLifZeRGM6CbPWnnl376Q9mwfsXOLT/+ox+4dlcXh9nitpB1Xzqi6O6lzQEE4CVSeIgKGkNgcVLVwm9TpUKjKxTOILE0e5qA4KtPKIXcTCDFysGn4ZZNfqkcSH4dL7UiW95ywHLgnHCgLT6cBJLCVtj28iz93rev8ukv3ZHy3J/4sffvGsBpIkDYbmGSDLrgvShwbr3u0lfceNNcpfmXH/tqHXxh1ZeHUgmfltn9YSRVA1qAO/jq5rr8bCFX6r7/sWcxthL8JT8OmXDGkMshAR+ZccZmF7q7e4cGR/Dw//WHx8iDgrcJJokgjOt8AFt6zPnyVEiDjCbgF2nVRjsIAWAxbDWoHKbZZes4G4cH0IYfPSZVoRiWNtknaorQ4tTUNAkjA/3ow0uYcCHd1pzBHufDv/qLH/yxH7zh8hHmJAi+GaclZ/y0Ws06JuFiPMvsAcMuPEK7nPbOsT4ceuu72Yyfz6VCCDEBRTRhhWgr1J8YkI8B3SStM54k2ojlwLnigAXgc8VJW4/lwEvlAL9GjIrSogAGuNJL7RTo++ys8xf/75Mgwauuv+qm3b2gb2Nhrovz6jjCj2yyBUiQ7/3veReAfe/Djzx9VPYmSQAlTSAbMi650TGDsrVac7rqzJYbgE2+VHzu2PgSMix5WcZtongG5lIzdefwRDBfrl11xeVbNm3Id/fc9cBDoCxSpyxUR5pyEmhdtuMAqAK0JtAIqnJO3EubM+qECJkKCLH9PSXWpKfn5sjP2i1JZKMWcekc4OBpidXX7i7ZVsTqcqtV68k5fUVn81Bxz9ZRJhW4gpJV6nqZCsFdHFEJ2su0QKRz9NhgObiOxjhkJoEwLtZhHAwRhUQLjadiKXaqgMEzeM1xQDActNZbC8OnYpVNOwcciH6i56AmW4XlgOXAS+OAbIqtV1g5DZsIbmmvq2fWcT7+xfsfe+rZbZs3fvc73lRCcJw91l0CoVoglqxMIidyikA53F1wXvfKV3OGz0c/9ukGYAX+LNbExtjxFlhGlQSj3Q7b7HKamOMs3UxXPjMy0JcqlL756BIbl+pLVYez/+SAW3FV8bW77u7u6bt0x+ZbX78dF3L79h+iOfbtsswMrBnoBf2ImnmAAWBSBQXZB7xY5izArqIcBySaalJB2baza3uOJebJqRkAmDOLwF2BZer0nAMHqgBeb3dXAQNk5gpOuyiHJUn/6A3zEomQG9lXdrJCI0ZSuGqSbhlqJCJbnFOs+KbEIEtCmOyjVfQlCTTW08RNhlNcyMmJihCjz4gk8VPktkmWAy+BA9GX7CXUYItaDlgOnCMOcDRQOmRbkZvJNbwUmuG//9wT937rse3bt3//d33Htj5E2GqO/UmhiHcsDINCxug5GC26SMa3vubVuA5+9viRz919oA0ygqbGZUTaL7C0y0/d7BUSR8rTi0uNIBzqLXEcQK3Reu7gURKz5EdSNmcTIQiPTUwtLczs3TM83O1sGhleXCg/c0RsugyYd44bEhfohRaBU8FUNiFxPJCogiUhCuAm4FrMpavNYL6qsqtcyU9HlsoCwJxnoP4Y6BwYTHHqlKvcajCCs0SJiPyrpHQSpPns1XJg9XPAfm9X/zuyFF40HBBhEu/CaHBTQOBjR53P3XHP7Nz8m265+doduDoEqarxNiM5/EcSWHTFzX3LKQbOdVucV9947cT81Ge+9tXxskHFNpmQKP2GseeSAuy8dR2OuGNBdPNQ1/WXj4Tt5uNP7cM9JEhXrmG/lAXesbmanBrnhPsrtjkDvnPtpbtr5eq3HtuHShzf/THq8RfzZcROxUitXq5mDdjnlDlVekuSnOYjnoq7S8VqrT45K0pg2QZslp85xJ3jkpBNR0eGxB0hqbJ7KqA4IxRCtaFaqpFJhGxoNslyz6OodRCaVG7jQLTjLk61fy0HVg8HLACvnndhKbEcAN9aud4+JEKg7p8+d/vCUuXmV930ltdtBb04p1bEPrbciLVyE1wFXhBZJSy1s00xzvq2m6/esnn0ufGjX7r/4YqPJOqL9yfAWnxbgVTyB3SbmVsC3jb0ZC7bxhF+2cM41iqLLVWl7dfNouxTT080a9W9e3aUjC/MG67Yk3HdR/Y9C2G+7v8xym2cKgJ61Ax5omoWUVaspMv1BnpnnGKa/U1EsbMSUEWT3N/bxdkJk9Pz3Kv4yxU8nZycZKl1eHjYmCHHT8SBh9ZMCh/pA0pyg7ksa+vwJVBtsknmKJvgtPkIKNtgObBKOWC/nav0xViyLkYO6F5dT2TQ8QXn4UcezaXc977tVcig7HLNpvGykXO8PDuNWNtUFIpghkcNJ1t3dnQ7t93yunrY/sx9946BeuKd38kETrcog5uNdqXRkg1H8+UygmZ/3hvxnS0bh2v1+pGjY4BnqtiL5E3kgYcepsobr9rDAAHSX761u7dUfPrQAc4tQGwVM2HxYMXeYjGhomqQVVZnHfE2RZQj/JBdiyzzAvdGUSxLr2bGMDgwwDLyBGfkmluuNIHrr/GpaU7nGWITsOIsDyhLGalP/kcB9MVlslxhgAkUkA9W2eYTpVKrfqJ7+8dyYBVygO+oDZYDlgOrgwMIbXncUqWwmXp6/yS63d5iln1HOayigCJ+rC6n42aaXs5L4YRRLLYQAzHYwosVa7McClBwnNe+crefST81MX64LM40xEwJ38sio7Kpp97gVHSMpBYqnP3an0eada7csxtb30eeeBLUT2XEnhlMfebZ/YWsv2vbBvTe5BntdnZs3HJ8ZuqRp6cAQ8FSI20iNJNf9MwCyZSTvUlEK/U6Mik+HY1YbAAU11IGqkeGBl0/PTYxQzapxjwkrhLwyEi35OMTybsmh15E5pbtUjTDVbLAEYFroijQtViM9ybV5NHC9mo5sBo5YAF4Nb4VS9PFyQGznyddZjeQ4zy5bx9+pG64Yifom8aXhmCbW2unFoNMO51Fq+uE9bw4l+SABMRBcDIFDPkVZ9Bz+vr6Khnv4EJdABhWCjICVOQkUBkq6FmUxwPdWQyMr7h8G6mPPfEkNskGQp3nxp35xYW+ntLmERfRlg8wfM2VV7ip9P2PPkpxAU6jglYsFASkeoHFSALmsAekV/HDQVYjE0sJs6CLkhlheGxinD4ShCwpJmvA3HLsfGx9bB5HF4PUZh1YMpuPNBgFosknSlKKSLUYHHPJ/l2NHLAAvBrfyrqniWExGhn1j9wbLaIBiwoilIlEeVYXO3SsX6ZJaE+6I8kxGKhEKKBGismB6tZgAvlBEe3dchy7Zs5QBaU8ZyF0jk7Msy/2NTde2yo3c14rbCHGspiL9ChGT61GK6g1cMOh6FZZmBPvUPya5YwhZ1P/QC7XPz5fEawiN41y4nnYYq8sh59zN11uNP1cT5EDZJ3to6BkgGdKLJx5RJl9ByfxxzHQnedUBGoLWhzU61x26caenvyBw0fmWk5NAJEZAARhnGyWlyUFrGdvcgsqcGaJc0o3zeRBHpiueiwE09xgKRe4/tR8tWoQmMyyA7jtTFfqyPT91EuSFGJuYA4OlPJUIBMJyFsZlBKIiSIrn9t7y4HVzAG+7DZYDrysHAByGHvBBhmXkw8uIPCDGDoLLWcaL8RTzpLx58Dz1RR0uREgiLAA8vSGK2If3g9lKVQ+9EU+jcaC6a5xMsVT435iqS6wDBNYpQWTI27wqN1MY4WFsth17v7WE5lcYfvWnv6i71TnAd4WTqMML4BDhEsOtw/dvMeCKKrjPMf54Ycx8IpiisWKbWPOqcxKbg76Cx08V7Dom820OdOhjNZ4fCmYrGUuuXQDsDaac3Zt27TgFB873MJWusd37vzWE2G+/6arL8dFJGZbXiCGyZdcUirmmhPTU48fk1cjHifbYSYsZ0FPzisOgVHk5EhTXqm3qq1UqT/TbJlj5+Ut+1CPhvyqndmlpvvcxJJYRRvrLTw2f/WJpdzo9ks2DKtlGT0xUjekGzZKx8V3NKMVvaUc13jkAnoxy2JVmA+RyECa8mTgQ8QGy4FVy4H4a7xqCbSErUcOKG6d0DOGWjlGwEGf+tkHK7/9Jx/75Jfvx+fDKg4RBiuF+C5mJZVVT1kgxd2/kUzDejWTyYoYChZwXh6GUyGyIYuj0cRDgdxk0LmI1EmnH3mylS52bdy4MQfUUIAF38jiV0BFuGJcWFAwIoIkv930UOUKPvXn8nm3uDCLJbU01MZZMursENGZ/bSgLMcaAcfiV4rqgc1LNo+GqcJ9j+zDn1S55YxNz1Wb7Uu2bs7TGKpkn4L49mhdfeWlOJl64LGD1CuvClrkCENRXbdTLg40jWWW9Bx75pDjFkRIJhv/IVMkW/AVobhY6m15mbklVQOIR82ZWqsapga7SxAjBdTJl0A09fNJhqlo0xE1xsjKo5M/ppLlPKZSe7EcWH0cSL7Zq480S9G65EC0seSkvqEcbQUtHPGy/5V9qc/sP3jomAzlJ2VcTQkRHvArQjGsXpnAA04iMGa6aTfXXXVSnFpbB7HIBIhyuCA5YiawrGk2uZo+mW0zbVxYOM4999yDjLv3isvEiqmJM+cIbqSOuP/sGZaqzC2LuJoMxwgDAwM4teBsA+JgckrOyiUje3aJZBaqdS9oduVAWwlcr758F8Tf+81vQdzhicbU1FRPIbNzR588pkaoxPWjIwcxgbgPPvggydIN3o38ETJ8lM8y8RB3zA2ZSrVgCC41jD5Z8kg28xc6Bgf6yDE5tSQeKNFW4wV6ZgblweiGYVoRyI9KaDFzXRldTrQxy4G1ywH9/a5d+i3la4oDgg8CqQYNzOCekM+Ym2avjVjVTszMDo5uSucLnMq+yoLZWhoLo/G6NSiEa+KQk+ARL9uB56fziJh8kOoqbQDYBYD5iIIUUIpP8jGYSVkxZ4qwyeWwHx8OPL3vSbfdvPLyyyiE5ZQiEnn0I6AYs1H5k/hKJAO1jQwP4vFxbGxM8sFvhWd0267f9nNTmEAHrcES4C6ZweTLL+nNp/0xjgl0nKcOT9aq5b2XXlI0RwSKTTNSNefsOh5G0T1d3VQ7ZYRXmU9AiRADzorzR9anSWRtGuceHMRAByMoFSKWw+jIoBu22Xck8rHPFmVnYmKiXq9t3rCBhHbkoFJ7K9+UVfctWO6KjVkOvCQO8P22wXLg5eKAnNouH8ZZPvJftpFIaC0sMXaLjtZxDh49vlCuTS9Ualj+6uNVdBVIEHKiDTDoglmnlHVfTucRIASVUh6OpRbbsp6Nw+KZ0GFHUIUiIBLa3Fj8lVrE9Cw+M0EqTuF9cXzJqSwuoTPetpndRQJRIsaawK1pe5mNUboYZoF2ciUMDg5y0ML8/Cxx9ewIb8W9NOK4mxmbXko7zZFuNumKaIsEzNLrUF83R+o+sM+57/H9LFS/6rqraBKnVwBvrYpdVYixFWZeO7Zs5vU99PiCvEKWhn0Mp7Cn7nhLrlflZKKgnc/ITuUoqELe5GJ6sWGoH3Oz8YkpniInk4zMDcHMG6iVR3Exea49Jo8xEet4YqOWA2ufA+bnvPa7YXuwdjggOzXjYXV5qE0VSwAI/pPYhIMjw6VGa3axzC6Z5Ryro4cggcGR+Icjzh/UfjtIez4AzFMWLZF3EXf9tNgz/+Nn7/vo393+Lbw9Ugg9rWwOEmARtDQALLhMnJ1GrLU6zsOPHUr53raNoz2s/Eqvo7z84WPkZkHOZCojOlwCWmJOkTfVdgOmaa/ZbC5SnTwyl5BpgtR/fGoh7wYbenFeKRiaDsSp1pWXXuKns1+599EHnjpQyPhX7ixiGybgHa3j0q64tbx8z6XpTOqBb91PVUKcsXsSZDcvij9t9kqx2dhpFzKifo5EX8km68FSikMJRwbZNDwxOc2tkjQ7P4fj6KHBIlWKsVby2k1/Or4DHY9MVfZiObCmOcBvxAbLgZeTA4yhxjSns03GWXFt7M0tNiZnEasK2UJXyzUHrXdmW3VxhYYIFby0nNZTD5yK6HoF6iarzt9+/Buf/PwdX7n34YefO4J9Ezgk/pENIspVPoFiKvuFKAI4f/Ohx9K+d8OVu0mHKS1s01JApARSYFVcXKYy3MlheUbE1HOJQMOC73QVS5wlNDGBhTVLs8Aca9Qc4yBNjM8sZNz2cG+BQsJ4VwD4pmuvxMDq4X0H5svtLaPDI3nBZp4i6aYEh7HQbpVYLb7yMiYZzx06yGkKqIoR9qUC3ien7ho9QDP08GSddgN02hQ3ojt6aaGaC3+oa6Svxwub49Oz8IPPUs0plyu5XAYhnPEorc1FvaREdNoSNcQ9N1F7sRxY+xywALz23+Ea6oER+FS+kSGZEP1xAuRC18vkMnOL7aVao9IKxGviKhtxFTeV8JVXUNCVBWxwlPPkiTx9NPiLv/3iHXfcPTNfSZX6yoEPaEl/xWOG4KIEqVFwlGSgiFQ++w4e9cPWZTu3gXrAVaWM+0d+p6JlFm5FRBBV+DcALHVJkMMAAwRpp1DIY5U9NzdHYgsYZBE3JRMaWuGUXwASJxwGtakmwIB6zyW5ZrUyv1jJFgrbNsp2IKCVgEWVBxTz4hqyG3jHMPJpu1KrTy86nF5o8NTz3RSobBTRbjN0aS3ltrOeCPyxBCwPRSI21fYW06yYL5QrWFPzqVTbCOv5TBb5l/6KVGwok+5Jx5UQbqL+ShYbLAfWBQfMb2Jd9MR2Ys1w4EQcQ3zi43HYD7iRcp47dNjP5pCulmqrzwbrtCxm6TcF2LAZFjkPnDsw7vzRn3z0a1//RldXz21vfbuTLjxz+FimJBBaX5oDoACTahU0NHZMDVFKU2oJG6gJp9xgp1D1qh0FNYMqdhWNCymyIOmaK4ziQzwUA64MgG9ETAXnlA9gOtu3bq2WK5zjW2+JS0h2PyGW0x6tLC6VWa8u5HNswJXQrqP1LTrOnku25jLpWmXxFdfuZWGYeQItYJwuwNlqk59qc7Ih+BImGU8fOJilDDOH0MfHtHE0idK6CRpPTM347VZ/V57JhFhac9QRIUZPKtkw4JQyqbHxyarxr8EbZwaydcsWaBOwlVkaFUoh4U/n2q+R+PWBvVoOrAMOWABeBy9xrXWBMVixIiZcBmfPxeAW7ePM3AJ2xJ6fabRlk2k0Dsc5L+xfQ3gnCWL0a3Ycec0ayldP7YJvv+vp3//D/3H8+PE9e/b82I/+89fdfH2t1T42MaU2ZdkcdsfSLxfgFJ0rMRH0gB+ujz83gwr3sp1bJVMb0yfJCa7HQWPAtiDTCUEwXdCKD/VwsC7nIgDAqPYFv9laa3ZVk6larXMiQ29vr+Gth24ZO7K841x7+c7G4nQuaO7cPAhlQCctkEfqRSQNcOkhEmo67bfcsN6WXdviBCvkpUX7dilBIqZbSPBZTmpQ+nRy0IGjMKCvm1kF/rBE4scxtZdO9ff3xuxo0ZYWpXX9mG4bevWBvVoOrAsOmN//uuiJ7cQa4ABDshmVo2FdbsXkxgSvbk6aPTY2LileioPixUfDKgtxD0ThbDoDJGFhlHYzBUADmf3u+5/5zOc+/8QTT9x0003f/77vvnKzt2uzwybXhUoZmyzxRcmaKduV6CKAJoiG4hV5T4Q7Eu9/+PFmq33jNVeKErjBiYLCDDbLxmyIIwL1HT9ekRrjLMbInDMPAGC2AtNAo8kRDJFISfmlhXk23w6NlighhUTfG6AUv+mKXRuL/iXDpW1DuLoEcWlXVo9NxeKziyVuassX2C3mlJtsT47a5JAmU7vYYgkT2pzjG+ZQSycUEQGA6bz4sBZ/WKODA0yzjow3WJM+PjHhuv7mDRvlqxGwh8msJovSWjofQXFUFQkxBzort3HLgbXJAVH52GA58HJxgOFXpEYGUTOsyxBtIjJA44SDRc+jx461Wz1tNwA2xITHAPbLRd6ZtNMBAILBYvYMSGBJBSCxqvnZz3/x6NGj73rnO77re27rFjeQ4mpKlcRsz3EAsTBE9hUARnuMMhcBEX+PAfuHU2UWgA8cYt/R5bsvYX9P4LHBCfUyB/BKKwknEC0F8Ch44pYdA07UKhmHhzm1sD05MQa57VaQSUsNBFhcKS/gW6un25DNFECw3GFT8I278t//zjdmM2kIpmK0EGxCkq6ZgswQmDBQe7FYQAJeqLHTytyL1t2YV8mrFAm4jkbaaRXS8XmBIgGbDLISLcTxGRrsDfeNH5+c8/YMHxuf4tWPjA6ZR5AZbT6jXZrjGvc9IkTJsVfLgXXAAb7hNlgOvJwcEIRgSJXR1Iy4RHRkNYpYZ2ZmRsQ19syI9LXaAoKm+UR0idUVqAqaIsnV8HWcdw4ePpJOp//Zu27rEQHX6QKxgMOerlajNj42WWmWQRV0wpSibCg6ZvkNcrQuTDg87ixWG339g6MDXSwWi18LOdHBWIhLZRpk+gLEmYLyZzkoxgpgOTibwggLZhKHnxTRT21RHFSn8l1komoRrVMs7DrFlKzvfsdrt7/p+k1ov2UPUShWVMtV4qDZLDl3dXVh8rxUobvyAiWYUSSUDgkAo7rAFoy9TCaZp9FjAWINgTPU349KfGxqhkomp2ba7bC/39QmeWQNOM4oBJAnDh3ROMn+tRxYuxyIfiNrtwOW8jXGATMKM44ysOpH6SeO68KZRTYB14vFIhhGuihqV10wVAtcGptnAw/ABfdobYk022HQavaaHTVsOEKTTE8G2GETimeMHAcEiQJAzrFXJsiOJXwzc6wvLqCfeCp0/V27dnUBhnI0hWwyRrFLk50h4puRvzvTO+Olkthelctl1o9BYn0E7+enZ3y33d07AAHUIwCsfrI4bmmh0u043a7DenZK9OR4lhQdhASmTLJULPOFUglle7hkJGBKR0EwHulaQL2Oxttp57OyDSkuLrniNWHBayRgVv2PTUzin4RDn3Cl2dOlOA760typxiXS9RO3af9aDqx1Dpzqi77W+2TpX9UcYCyP1KGQyZCdDKpISsePj7GhheNsS6USclu5vDp7EuGOUg7o6ieNBZkhWACPAwpCORsI7XKz5gz39/i+CwCblU2nznkIKgHLdAR9raAhtT36+GNofa+88kqZfdQrIgiiC45aW2YFCSelJU/lCbUWXHYiFdjeMzdXAd6UVH7t0+PHM6Dd4CB4T6KpRweBdlcW46l6zmvm07KpiL1FooU22SSrOqVEo57PA5K1hmwDVsyUtmWNmF4IK1jL99xQbLO1fpV8zRowKdCGYD88NBAiAY9PzswjMTchFXW9MIOZDUgv2C1t6oe4DZYD65IDFoDX5WtdzZ2S/ayM61wZrBlhictQDQC5zvE53CcXerqLPbk0AFypyP6c8x4ESRTUuIIiAiSolPkYBBAjYVUFG8ABmmQZm4d0QftCF7RHh6YbQb6nf3AA6JVdO6S7bBNyhgcHANOjY7NNUfT6bNFFzUuvjQYaX4wsCafp6pHDx/Kthcs34ULDcAfhWCqWVuIgi6g8XW5eH0TSMEcegd2i+0Xv3ZVpl53UWJXzHYDyFi1S1+Rio53K9xfSiOakmLVkbMPcVq3hZXyHQyFb4lATliD/NltN7ZfZjcRqtNhXi5PngDlDPDGAOFLhSoAobdTNIitjEC7rx8JaMsjsgv3JIgOTl093dzdtTM8vIgJXnUwpmyoYeqQ34gDTFDRFyQwNXKUequdqg+XAeuGAfLFtsBx4uTjA943BlXPkxaIHoGOMToUtP6giBrL95snjS27XyLaRgWyrnEtn643nEfXOEclAAx/2vYCykSeMGuLckuPMAsesQwc1p111GkvkwssVRyyEolSWHw5yZSZ0cnieMoAHTiz4mfF2Jt9VIp4xB+RKNt/p7+1rt9zparoKTmEG7dR7g1YWMTjlNHynni+VHefeJyrtpnvtoHtlSRpw8r2O1xW2fZxCRygojZp2DSZp/42kDSPBJc44RPL2WKgF2vtxmtHntPo2fPN4FTVvq1FPtZuYGB+sZOf9nt0D6Y1Q2AoL6bDlpmpuIZUHsnkZaT6BK0u5XiqTSflikIWeOl0AttFcQNhgLpMHOydmyC5TFZjGK3UB5mLQCmDF8UMH8LrVP7qlYtTJokEXgRoztSbEQSiVD/a6uL7CWusrjxxfdEt7t23tpsewuNqQhXQP9b7UTW+Fk8b62txFHJBmbbAcWPsckN+zDZYDLzcHjKcFAI4hVxStAn7i9un4zBLeKUYH+npy+EoKKtXay0SY0AE5+pGF0QXo8pwGUfAL8ljjRDvKplmDH4ZsQ73ERE8MjAItiy2n6mdTGbTRcoI8VxxgkGWgpxshcGZBfFLI5l7pfjXriispcKZhPs8dna7Xmnu3DPWxz5aCLp6g02wuJi4aAg3cGLA1f1VMpHo+5odsiIF4ngKEA90ga2a8Kk2gTkCIZyowU8PbZaY7E4K3ubAG5fRIHlADxYxwTzVUooEMKI+RSZs+MrNkyXkePrRCc4yg1OrJCjaVyLzKFWqRttFmgMGUjIMylgTss4RWPl2lopdKP77/SC1w+7sKaAakVbZqYZAm0xTpDBUgbSOm8+GeVkwX41rtX8uBNc4B87td432w5K9BDkRjczxEy5jM/2PHjjUbja2bU92lAj6QF8sCUS9HkHVKoUE/iGxgHsN9OmeEXVTBfqrV4Q5jJUmmG2ADNk9AHSuaZNClT7ZWAW4jQ9gFtznzxwCbERiBOoE9CWAzGPnUU09wit9VV1+pNlOiB45NwYlozpOvtMxHHmusI8eG4REWbmmUNDlnisOmAOAZTI6bXV1y7IE5vEEoEth8oSAQbkI2i+oax5SyjE0gndZlC7C0ItdGq00iZnTavaQgzcBYyWECW4Fxeb3/uWfYBzU6OkoFchChmQq0YivoKKupvGNWECXbP5YDa50Dy7+Htd4TS/9a4oAZzDu+fKgoxVBocnISyXfTsFPMZoAuhvJo7D2vfYMYoQdy9IPQJV4hp+vmvAF0s7KjlWN5lwVRISfCo4Qy6c38/DzA2V3qkookwYieonGl6nBmfs4Io6YIAIzbRtMkatly0zm4fz/YtmfXbvNYLmzEUvSSDdJnGExfTMuOoFoQTJhT/3AEjTAJM2dnZ3nah5U2QUk0NJjqtdzKlpQG+qUP8qIh9nAtIlIvQfaMGQNo6jE2ZmAzyfSFW8MkmhX6tQbtCXWxLs6RDI1KpZhPj5pNwGYiwBPTB/NnmSDjLptZjKlQmrXBcmAdcGD5G74OOmO7sGY4YDwpRgMtRMvA7c1UnFqtVsxne1JOxtjBsqXnfPdI1iQJkVcpZDY+ckT81+8e+79//ZVHnjwg6MtBEUCheqUywHAKqoyQyuEHwBUWRtBNxaSpBTJenYs5v1ZtYFUmXYpsplBrS000xxlQ2EgP9ff1dnnArSIuVVGcDM8jAZt2YlQy+Cc1UiQIhgeHkIBnpqZFcyu4JhuIFxcXYS125vLLR6g1W4RNieQS1yYJApcRAGt/5JgHScG++pQvhw7VOcABTXVGWBm1YvqJFK7cxp6aRxuGB9xmA4vrTCrVVZLG4hkONtgs+xICEa71I5QIMTZYDqwnDlgAXk9vcy30hfGUoTwe5OPvnzimOHx8ASFpsK+b5cCinAGAN0oZys9fgBYGdUEFoYf/kIOYK+a7jzz19B3fuO/g+BRyahO5S71KGE+KmtUAaUyawTduVAIudXfFeQyAiX2UM9TTxQ7hqTkDIyQrBsdbdw4fHUcTu3l0BCISDyRwQ9XRCoFxY8t/lWKIFvI1xDHWbgd6eljXXVxcMm4lRQJFzm4066xQ93aJmZfs+YEDcjmhN3LfWSd54JTklKx5OfrBxZkmm6dMUW7lowHW4YiDeCYj3JRk0wp/jTxsXrjYZTsbB3u9sJ71gozbKmJApuXZXS0nG0owWTVV29G4vVoOrB8OdHzJ10+nbE9WLwciiVNFo2SUd8U14/4jY9lsfnSwH21pl7gS9pbKGNKe36AYbNqQ34IZ+uXc3NklTLCxPIIwjI5yZlFYd9OQq4MoBR5ET3PYwGK5Albn82KvRFDJlXpB9ZHBPio5PjkjhUXWk5IKafT94OFjiP4jA708peMEyoJzBPKoHCw1nhSoRUngScxbSeE03u4i10yr3qqiSzcW3jOLKIvFS3MJP5UG+2MaT6q3IwEaFBK1OwivShUW4siyiNo0p/ppjK+g3zi6drJpEXNNUHaJ/C2HN5DZle1XowOFvNtKBbXuXKqvi01KJi+HFhtHJVpSrorGESgvJ9uY5cA64IAF4HXwEtdaF3RINSJO/P2Tk/LGpucR+Pq6CgBwkQVE18Uv48vUNwUxQxgXAHh6YSnM5sDghQYCnUBMsyG2uRITlFFQiaijiJoX6fJnBunP9A4NKvBJEfC2r5jFMHh6TqcUPMFKGiSSp3RyYmY+l82UijnMjxRuEzlYQfoF+aBMjbBOWseKGgD2Kc6xCRiQ0cpCtQqYZlJyDIQB4BN6IVAucrkE82f5aScAawbAmMSENnpBIAUyjH20WFNRidQjErBmN5mYfhgA7i85eT/MBPXeYha3IVqDkaaBau4QqgXeDasTSiLyoorsH8uBNc4B+4Ve4y9wDZIfq3zNKBvRLxLwsYlpUOey3Zcw+hbS4iWxWjfuls5nH3WAF9xoGjTB5aKxST4yNpnv7p/hgPpMxk2zI0hEOux6+cFEUAFVJqayr4AMplvlMtZJoyMjKGt5KMcZeF7QDMDCjUN9iKgT07MN4N0RcbAZhGnjcgNxEOmfQ4quvGxXZDoMZsf+I5PIC7FBdcmaK+rW1s2bMqnswcNzACtJh8cmUYMPDfRDm88pgsbbtuDoCROKjjHBoDoL8+QlRAvSqNOHhqBqYkJs1LGtRmlMJGi30dNz/HCl1mi0WwOlGIDN2YIiNMvysHQeV520gbvN7oyb95rXXbaTRfYce5ipRlgaqQeIrUBfVgAMReaJvVgOrH0OdPzY1n5nbA/WCgcYRvnIl09iAlboSOcrDQB4oIcFQaeQER1srSnj9fkOBkYZ+o0S2fzlVMGW6zcdv+mK02YBUhMg2GQ+JQoYTG3KkU5IwECRSIgmO/UCKQNdWA/77HOmMjos3RchX3Issn+p3sqm/f5u2R10FoG1Z/lICeCNvyZqKgil0d4u9h+H8/OL+BahI8wOaLKI6pcshu38ja2bJRr1z1QgFyETtxwridI5AarmzkcalzeGFyxTKcUE900lqiHQ8zZUBY0GYNvGwcrUWDqoF2WGgwmeuKE8BRmGHmoQZ2k2WA6sIw7wI7XBcuBl5QBQQYgHexlxGbTLWALPl5HPRofEDxRrwOgfXwYJ2NDCBZxYPiVxcYlD7dNonhHAoU0JBkfM8G/uloFuGfl4IJtzwiCfF5Mt8CmdcjnOwBh0s+uml4MWxqdmqASQbrh+hphBQSyzyrVGfyE/0H/2CAMlsd44ppRK8BXZ9NOZ4cGRsP3Y+MR0vb2FncDTi2V6gZsTyJNgkFXhtaND+oyrOsMQIV6TErjlmAfiVVFodyW5SREOiNmcxyZgHVmiSQ11hZymJAcH89g8DPKu98prr2g0l67ctRkVdBovY/j98n2+ELjAxllJUrNEXLGM42N0ECc8sTeWA2uXAxaA1+67W3uUnzimqrwmafxfqDrzi1VEq8GSqCC78xkG9Mp5toI2HJRVWCEBeIAOPq4zN9cELUDfBnpVSdCMSYxbsCaCJX2IKIuxcaVeA0LYKWuwFSgRY2PwCwma8xiQgMcmZqQRV7Ya8xEvYCwAT3OIvdPT14PfxxcTDM0UlAqZOciV7giwDg8NoZgen5gCfaFmYmaWGcVgf59k0ewm9vyXBIAVsMmsnkYqlYqCt2kK5gifOBGZtgFgbuQ+5qksaWNXZ5JMTvyTeDddffkll2zcsWGIN86TXNpHn09vakGz6KG2Nx2SSljRlrOneERSpI6QdBssB9Y2B04YRNZ2Vyz1a4cDjKQmRDIWoypSYLXZZgct466Hj2LcEPs+i4nJGHx+Ogf6MrDj8NmYD8VkzczNYYKMLNuMNuCYsR8KyBCZBUXkKGAokeVyyO7YXC4X2UCbLKKNdcnV7C5kgaXZxTKHLgBVCl1m2dMZm8BDljs6PAgInfUPEhX0ciHdLkTDrJZKC4N9sBPnG/MgHAA8OT0HEI5wEpH0FKpZ/m2buKH1NBdehPTbPNUrAAzocoxgBKhxQZ5WqwHeM1HCK9Qm7NJK4l5zRFRdJiVdmUs3DIkTSk67wMZNpisSsp6wUNsyvROucCuzFRssB9YRB876976O+m67cgE4cBKgSgJj69GxxcDzcd7ENzLtNHO4FWYf8MthBQ2CcZQt8GRmA2bUn5tbQG7FmhdPXDEMGMA6mWHLj52FhQXsjThIUUW0jPErAVCZxeAWtt25QrHZCmbnpMvgkxQ1MHXwyHFUrFs2bzqJOSe3d6qUmAbzl0v0F04O9DkZLz27ME8S6DUzvwAxQ4MFAV8Fa4y0I6hMaj6RCrMbimdyLnFUtZxISL9EAjaFOmuo1hGBPbUDl1aiwASjY6jhoKVWA5fRJKWE7TWfNXfQN3SaTc5AhiuizV4RoJ9E07cVT+yt5cBa5UDHr2KtdsHSvcY40DGGLg+zR46NsWa5ceNGxvRUykv5jPBupYa4eH6DkXzBAA6EMHQZipDtaBUJ2GBnBwEdpEtqxy3lFhYXobmrqysqwEYclTQ5Xw+TKN/hEX2cmZlTIMHMG0GYX+ChI0cBqM2bN78U5WpCS4KnTCr6e3GIkZmfW8RemcVZKIS2vj5DoMKmsDmit/OPSaNKOCMVc+nccYSUT6KsAeuzjpKNunjD7rDc1r5KDmMBbbKGbY4aFo1/2Ew57RwF6LnpALrmuDIYE49O8TbrpI9xHvvXcmBtcyD+iq/tXljq1ygHoq8fA+vEXMXxs8M9BQNCrMBihOVi0yQd4/Hy0MuAnnzOVa8hg9akFVTSTdepNPFMHaaRZ5GNk8ahwQCOtApuEjc2zKac7LJdqnNaX7MvIzZL0jEesPAJygDAHGLoOYViFtsklroR5gRx+ONl6OHkzGQYNIZ6ehACpYWzDVImanC5OIumodPrO6VUvVorLzXEzK3WCDh+sAf9rpCI5THrsOLrks9yQZTXEQF0zxPLKfKKETOcELCmdM7DoipdCdRgiufiWIMq6FA99DmxkcOcZVlXhWxakTMcOb8B1hpCaTOdQSltDlqkWioQNlEDxyGiNO8gKeIzWTRRMttgObBeOCC/LhssB15ODuDCkGA8CTO8silHNrAenOYkoeCyjT1F/Cu12r2jQ40265OucWvoBM26Oo4I2ghyZGdoTz4x7SfgdJz4An8x7QHpBQ8cDsvNOY2UMxM6x+cbfrOBp8QGZsNm9ZRVXANTOhFgb5LZniSOOcToCeBBZD6+0Op225sLYZ4iDTN1MLZPmWyx4WRB6A2j3W5Q2X/oGEVynLHLGQxO+kjbWVia81vlPVuM260XIPikx9QFbXJhfxNiNgbYhlZOekg7ULJnQyoIa/uOBFMVcLAwkAn6DMGVIN1283Qf1DeYh7DLR6rSQYFHbWyTvVQraPpuE8AMWi7oW3KcLV0ZJhHTTY43RnecYUNxo8ahjcKHyUq75Wc2DfcgI2ddMYlu1qixyDvrzqfNDAOgBUzFDwkA7wmg4zQL2nFxKa2Thw/4zWkVMtEhi5nokI+PIL0NlgPrhQN8z22wHHhZORDrPkXskSHXTzFOzy7VMBwaLGZK+G7itFwk0XbI+XlihYwEJUN2ZwAoKMqoDljotfPpmcYpiedIsFwqw1aKmCfGSnOVWioEoNgXE1UlbbiBkQYlyn8okPVLA1lyXC4GwE03FbShH1rRrwIwrP+apVavxWH0cgZRMeO3FxbZZizleAhKTwDdvjNYKAgQmUTDlDPrAmQbyCS3oqaQKWmGXmBePHDhAdNfrLXCSqbeDLYWM4KCbA8yAquBbQpAndbFdbl96qYiOZ9XWiGKDytBwbxI16lam63SJgMSMNKuqbDWxqmZlxHwpFvygoFw+KMUml1JSrE0FAdDe5zA3+WXHSeSczkxLmb/Wg6sdQ7EP9u13g9L/xrhALgkm2RNMHY9IuWUa87C3GzW9/rED7QE2cUi9kGuOdrOjPIGt0USlSA3yUeTXtwVWloslqIgZgeqkc1JmZ2fowVZuTXepaI2X6gBjJJYM9YtOstbdwx2gTlUu2FoMOX509PT+ExWlEMsHhufYr17g7E+06nJC7Vz6ucnEklrfCT09Q+6fnpmbn5mzsEEjHOQlG/6+ExaNGAqVSXLxewD5pYFAsVS4kl/sQPnVo2wNH/yiHQbLAcsBzo5EP1KO5Ns3HLgPHFAhn6EIqNRpAlj4gQA+5Mz1Va9wkadkhj3RAM9B7qz7lg1g7xIwLLfhjLP+409EYUk/xkE2aJKCDkHUNCSD6cKAhuQpwCsMLPSIOukmjnpjzR2UnGNUIeuipgfye9Dfb1uu81mXN16TCpC5dGjx8N2sHnDBqnvRdEvBU8IsEhkdAKU9/YPs79nnHXmWVQM6d7eXtIlhzyPG1QCT9W6Cr/SC3kvpgwScGwFTaqmJSiL30oYpQhtATjil/1jOXAaDjzvcHaaMjbZcuClcEBH/+ibJ3pLDy/QKdfZMNhrVkTADhF0u4ol8I8BXUx5zF5aGlUUjFDhpRBxqrICMmbFt1yt0CJW0C3F5ijzC/xYAGDISwAY309Szkw36A6F+4ocvtCYX1xASGyb9U2eHz02xiaoDaODImzHCBc1eCZ/zDr0KTPSHWYWPQMDbdc/PjEzPbeQzuQAYNL5KElQtTyxEK6v7GMnACt5dChxxLHcrthySVAABqHlhqzPe5ST5LHBcuAi5sDK39tFzArb9ZePA+CaEYW5yOLo0bEJnCBt2zgMAujxdpDS29OFaMi+UjbjMvQbcUq/rufsS6sViTWuCWyeAQQX61iBhXiTwAia8xJ4sgyLgl2nbp0nKgEn25BEeqYkdRsUJ8rxA7mUj3uvxYZAoEw06PvxKcTVLRtGTVOdjSlRL3QVksznpKKa3D8wgo5hbHJ6am4+9FO9vd3kF75HhZb3F0lS3NWkk8tzHaONlzziCUs0FWxDMtWwzG2mGuZRpwQsCcKEuFKTwV4sBywHEg4kP7QkxUYsB84jB7ARYjwO2ANrMBURjVH86OScHza3jPbzdRTDWFl/ZdcsEnCryQENgIDxUCiIYuygDAqSN/r2KtJonhdBupzOR2FkNY5gCtksC2oKDWIWrO4nzqzSzjVgSkSSJdZkzQZdRrjvzTjduRweLifmxNQL1MJAeGJyFpcdW0ezMgt4yVAlFch0RXTGXPnImm8qPT2/MDW3wFER/X09rK/jkllymrAsAccpJ/+VPEYLbS6OWQJeXgPurIE1YG4jRxyCv8vYfHK1NsVy4CLngAXgi/wLcGG6r6fBIxoBtCzyYgLttupDnMyu5Bjbq3w2y/DdxBuzkcIYyGUsFwH0HH9pBYwUWBDHOU2vjsTItl28/4snjoRBZlfr8zXdaX8klGpZAWCMnUUSLrKHJ5cJXH9qqUbH6U616cwv1LJ+arBobNN0KpE0+cIRI2QbxogcTIivKuOCssUc5mVppNVKuQaFXQXZaa1MVAyOMZI5h7a3fOQfCdFeXql5mRV6UqLarJkny490QT1RKnRis9Zur5YDlgMJB55vQEkyndcIP2OOMU9+zOqESAcFTbzkkktQP2pAv/ebv/mb5NeJ9iOPPHLrrbfyiDy/8zu/w6+9XC5DLXowrsmPPx5iOIaFLRg2XEgOyCDPVpZcRgQ1lWs956nnDrXr1WsvK5nlUUbzIJ9xKpWlUldhamY6nXVxoqRjPE6yqAARLrrv6MqLE7WoVmrm1F6jKObQ38NHprjlS8X3h2+g2bQszehReh0NnhAlG64oKTI4mFaXT/G3Lij0dNMEcws+l2ze3AjCoxMYRInvjqeebXSV+jk0kO+l7B+KbKdOqPmFbyLg1J5EV9HwmzVgio8Oj/DNP3bsWCaTGhrsbzX0wAbOa5KfSCStSiVsnloeEDSGUzIe4NpK9ukKE7jI2b3FYpGfIY6tmUmkjNNNXS6HCbTFQrhkDEM/m0WNIGVssBywHDiJA/KjuiABoOU3zDDHZFkd1zEYzM/Pq5UmOArWMpsGSsHaP/zDP3z/+9+PbSo/70svvVTz8/SNb3zjW9/61ieeeOK+++77wAc+MDIy8r3f+70zMzP9/f10KrHMTBzjUcMF6axtNOKAoqhKbDI+ey2OHgqcWtsrZry872Rk+5EIvOhIc9k0GKjemEVDLPhwYqA2gwemjDzSPCbtxJxndqdAIXM718nmc9UybjqiyqTp562Xg+j5AoM94t5JtMlxMH6lSFBiuwpZXF7P1eq0RX3Ts0uttjvU208GKcRxuAJmKzsa13XyX3KeyBjlMOhrAJi6+HR1FVMTi/jJIJGzgGEyTVNSVPsGUYFIFP8STtFH5crKpvnZUpY9R+KboyOcIAGbyrWJjiw2ajlgORBx4IIBMD7rIUFlX4YtAj9URV/S1ZKFzQwEltZUtsBZLo+AZH7kpH/kIx9BscaVqnbu3Pn000//m3/zb777u79b0RfRRXGX8ZRsaps6NDQU9dv+uWAcYEDXMZ1tP2HLd4/PcBhfOFrKlzynmOULyck4gh6lfA5Iq7EIDKnxUYHyBJAwGc5JD2Ks49snWlsowwSadru6u+dmJxROooak3VNClDxH84LImy+WxBmVKbACeGiIz0B3NwA8MTdXNX6dDhybaLbc7Zu30G2DfQjSL+onqbTF6Ev7JCioU93wYH/6wHjYaqZTYVfBweA8yhBrnemvTgAM4csoDMHC/FMFfoBMcKucllGKtm5rLlVNJWvA5k2Z9k5ViU2zHLjIORCPPxeIDfxQ2dIA+vK7Vb0xhEQegBmJzaZ+MoCye/bsufrqq3/rt34LR/D68/7qV7/6vve9D2RlIKCGW265Zf/+/eAuYjSVAO1mWBG5hAxckaqfeuqpC9RR26zhgBxyY9CXAd8cs8Pf41NyFM5QXzeilCiYMaltidVSMV8A0moNERYVWSImyg3PzVc3fmAqfUlMxvu0VsZsD4PkvsEhlMKNFiZT0XxhGZdO1Y45viHan6PPkRG1QrYzUQU3mcAZHuhpu+7RqRnygG1Hjo63Au+SLdsAS+lPNAc4VQNnmEaT+sHHlRGCqXZkoJ/VdpTshXwua8yvYB+/iGSKoL+UM2xBs7HRiA6ix6KqzqA/Xj2tQdJVMd2Zw8YtBywHYg68qOl2XPil/EW2IOh8mZ+riMBmgZYxl0DN4DHp/J5/7dd+bcOGDdu2bfvCF77w4Q9/GJT9/d//fTJMTEwoEk9OTiLaDg4OUuGTTz55zTXXUFa3KqKO/oM/+ANgW1E52p74Uui2ZV8iBxC2eL/i49esegoAL4Z+ZmR4gO+igBBoYL4A+UIOCblW5ax6kQqBFbMdSYFXiSD7S0feGFiNBEwrcwtLrDjzdWLexnxuBcCcrvfoaXjEqgo1EOiEWRkFyvk6C9KJaVMIAPdj4TU2xQHA0qMJ3FO5qU0buwUsxYejKXw2F/GobOYjKwtRIYpl4+tjoBcf1Xi5zg70oGUgYN8tTGbOoaXo6criL3SvAMxkxXX6Na9I0eZnS926DZhbfpJmgViz2KvlgOXACRw46x/eCaVfwg0SKvAJxGI8xWDHEEUgZWBgAB0y8csvv/zRRx9F0/UDP/ADb3/726+88sqf+qmf+rmf+7k//dM/ZVhkVEPDvHXrVkjAux5XSjH8oW0mrsitib/wC7/w7LPPIv7yiAiJNlxIDjBM645XBmwzZB+fnm273qaRYaRAWerl3Rs8yGWymOBWYwlYEMvkj4gn3nn70ruEHZbxhIWjDP728SXEw7McGdgRTt+iWv8x7VPA1kmklgSQAGBJbzuDvb2cPzS7uMTu53LglCuNTLowPCBzkZB9xy+9S8IW2T8lTeOvGZa2HbQLLgcqeOxBEtso2VxlJgUib5sAy6PYGf8BgIFtNZnsLBRLwFGa8iFupzOjjVsOWA68uAWnc8E3RiWt5vu+7/ve/e53o0nmt4osi/DKgu6sCXv37kVEBk3JyRjBI2ysEIIxfr7uuuuuuuoqtNBqckVZZF8glrJkxiUCq8hm4HMRrHt6enQ9WCXmc0G+reNFc8CIZoz4WDoZ06rJqTnO6xsdHjQ1GuQwuhBMdklhoobgK7ZZIAdfmbNGClPrC16w/o33Iqks241GHGdYRoNKsyDa87cMAPMlBJZUqEy+3vQHx5NifE8tQQgC+pnsYm1xsdEuTy/VW+1iPt+Vl8qZVnIm0zkKZirB2gtumX1noI99zSFLv73dJRoS1gP40h/mQCzE89tKsFjaf/6eKoX8lABgne9qil5l2cjzM3FHYCB9P48vrrNtG7ccWGsc0OHiAlCNrMAgxTISO4hontGH+Pbt2zXOwewakR9wOq3qaJD4wQcf5BeOtTNPN23adOTIEQRfHQXuueee3bt3K8Sq/lkBmMElsYJWLTdl126Ipi0ySsbCWQQODLYn7OAUTaOEC6bkODWTAQV2wTDGByHIioYXKQp3G71dRSRgZFBP/BLL6ilnM6TxRtXCJaQIiHxTSeQZgYg45FBemO5zsC3y4/MHzb4iDy1Ruahr3RTEYARVb7Y57Kcn42TCFucyUa02KgUlp8BTzFP5C8nUvNhogWSFVMhsUVJltVscafmen1J0k3KtlJ+mXxyytFRpT8wuumG9lG1TP0WaHP4nSB3XTfYXCtojWqKMVC/kcWfeu+r5jfa+p+DkwkbgpHoyxulzJO9KQ0waIBso5Wdycmt8x+K+A9jGEM5koiRHAqecdr0pjsyyYrSu7XpwAWoA++hNBXReFtcjCskmRHO+lbROOEWr+sBeLQcuAg5EP4ML0tPO3zwYqbcgrkaAT8YFhF101Gw0Onz4MJuRfvzHf/xDH/qQ4igbkwDm9773vUePHv3rv/5r9gf/7M/+rJpPA+f0aFkKMWuKF6SP57ZRxq5GgFUw/cGXVLPFUaucYstZcq2ag7cHcwodPUdvKqMcyMQ5Pzocn1s6XnxtrKpyzqw5x8/P1pHFOEF2YqynkO4rZnB7xaokx7mX2yn2yPYX8cTRnJ5Z5Gw70BeZKuvV3Tan2oojZTKY5VnGffghPjOkx6cijET9KER0XsnucWZtu8p3pRE6TU9OrT86NuW1m9dsdvLVxVpDDuoTgHRxzwHUUpWPRtkPIQEYlYOBqRyQPjSxlC31DndlC0JqKyXrxwHHKRraZcIhHU/LJGHLUH8mTD17eP74dDXn1a/e3Q/q0gM/X6y0s7qme6p+vFCaQhk/aCHXw4VnmHboFMmwbmNXunls3xUb+wQXAT/BP/l4fpqfngCwwvdJiOiGsnkJENUjBXViQcnR7oxTr8xxiAbnMLalbfg3u8hJhGl07Lgc4ZW16418V7/02eCxzztSvZcrtRl0l9+pDZYDFy0HLpgEfDqOIwer3o+pOXE0e3/xF3/x67/+61hRve51r/vpn/7pH/zBH9TdRKOjo5/85Cd/+Zd/GXU0y8C/8iu/8qM/+qMsQSFRsZB8uvrXejrSChAiwywnrvMnZP8p94yASBUSlVVLxDmMgOQPN6sriOGP6J9lhy3EQTD+Jt12M5tJm/6IDIpISHqOnaZ4xxAbYkER01PQkPFc5OSoYwIOJhpK958nRPlNi2Skfq5RtdImwC9/WJcFXrPpDIcJ5MJWORTv0BLgM7b0ImfDdWLCdnxaQY3Wxjm47dAvGBlWeifzx1ioFBmTYoAOzTrdxULGT8+Xg5mZctCqbeinkJh/M6VAPqU5wcgzDiv6FVHjOblSkUdo0OF3JnR2bx5hVoEXSumyfH3OPAhF/BfS+TrBbfnaOYUUxwQ3G02mIPBBZHwuTfYx0xfjLYVemJm0vCl5KuXFGpzvKkyTu/j9nyU9UtIGy4H1wYFVB8CyhsQv3hzOyvUNb3jD1772NV0GZrkXM6tEI022V7ziFZhGq4KaWwRfjLYIKKXXq8EzEhXbdHCRiNZPghkNZQhDqghENETmQsvrs8Iqg9xZDeamwvN8QdjS4ReEQulBa7w1NwiKeVSzKJsNuhqAyOUzQdDWbcDLRMWgpr1fTn+hWII5AgFm7Dcp3EGOyIlaIScKYjlcLGFhJPgBkZCkYhu87mxE9TRJCl858uraR5JIhDJmpiEvhZZpr39gyE0dHp+eO3rkGPOLLVu2SDZDwAkNdNZy9nHIkwbZ/oT/y4zz+ptfdeWVi3t2bzK9PvvqTiqhP0k1udJ3gk8dvdVlIBoyxnTK75PK2wTLAcsBoyhaXWwwI56QxM84sZRWEoFewJU4U2x+6gR1r8FYAAaTn7IUIa7pWmr9XdHneU4zggwREEWRiKhTNZIFRjcy6PPfWDmttu4b3ALwBNu44hO53WpgzyMbV3QgZ62S7sk+YM5DCNkHHAtMMqTrhMPITy+mZ3BFpyTxxMQAMIhspFWqxVUaUwS+P/jTIAKR5IgBOGpROK+kdpCA6RY6m+UvnvTTZEJnLS/ENCRSsDM4OMxU6cj49KEjY4DWxo09Wg0kvTiwOgU3RE5FrR1m0CKYLl99xSaE09g0qoPuM4uuaII3wYFIMIde84gvmiyEe4FaY7DbmEYl8D2Vt6w35toZ70i2UcuBi5MD52pCfM64B44i+DKWIQpz1XqBVSIItYArAhMADBIz2CFA6PYPxgKGSxCa4itEk3NG2aqpSFBIxBsE3rYonP006lEQi0N9SNaxUuAN7rHuuAoDtBl5ENJwIMVLzKVTzBsIneNzocDoHTLNAjmkUx3PVuCBlDQBZvBZ8ZRy8UfXxjXXKfJScH52hjXMvu4e1MJ8kSBA8um+qdPYeVGKDwsffF0jV27x91aIQm1r9ORSm1Gg9w8MMKcYm5qdmJsvlEp9vYZgFpbjyYH25aVew4AzlOEqXxTUXMxbsQ4j0sHFM2sh9g4NHyS4Bm2RqkslmCMHJxuNgrn4S5UqnSzkmEzFDa2YvEgVneGsyeksbOOWA2udA6tOBQ3EMlSBr0ApzOVHzi1xlYaBXhKJA8Oy59J1UUrLOqH5nePqmQj4vdbfyvPQzzjIkKr6ZSAilU43HB/0rbQxGxZrVAJLkAIKnK4nf9Glyt/VEwAqY/QjFBn3FTK1khsxixapUURMZCyThivKJkuH9FmQgL4IGPK2ycf9cpDV7hMSlh9JLIIPsyiuMNzxXJlGtSIBzxkJuMR3CGZigkR78u16Xh5Su25DYlJIJUIfvYheksaogVSmSkjAUuHMwhIwPDQ8IEgvJWTJGOCOlhQ6qDvLqPYUAgB8idMRDl2gM1gm12pBLvc8XDqLpnQXg8j9phBXelit1rljNcH0iFV1ZbVxMSZJK+qHEi29It3eWg5cLBw4N7/Gc8gt9MmgLxt5ZXdKfBQNiKuwqmIxeKyLbbpgzIipt0jAWGMxCOpWznNI1eqpCnPZCAvawC7yhsfBT0cXgk988d4nDuPCSYKsXxp7H4NYq4d2oYRBGJukhKZIaan+K9rsmGVOJU+R7LOmF41mdByh6Tal+cae/KV9/nHcPKUogCQfbVzuIzJo1tTJPd86cLdUlDP7oARcBBQFgE27cYGEfIloIhNHskV+OBSAk1zcypyIlyVdG+wXaK/U29lcYcPwUJxL9uvIYQznKnCUopCOqXiQS7lAIlPX0otA3+V3ZSiLWQCQ0196TSr8BeJ5UqtjSO4UOL6KVMOE6HquOmXrsRxYXxw4eSy7wP1D5QgF7CYSnwbx3iQiCsBAbILK5ASqueqQTSmK8BSBWPH4Avfk/DRv4MlgiVHLs/Q7W3X2j83+xf/71N0PPExcRkhghqE8dNviXGnVBYNnBt6Mu29eXyYrxsZsDEZCxQKa4ZseqHJGzwPmqRn5I/QVta52qwPLY2g4s/5Kea3DS+qglXq9igyKFbTUIlK1nAisBD9P/TxiLki2xAGFEiEYJJMK0x+qMqklFMJsoTUHigz09ABfPDdr0GBwfOyilj+zq3Yjzhv/omErtvGYQcNKkURDAFNmFXG+s/jrxjbnUlUU9GDP5MfIA3rHLdOIFOckCfO0ae10XCxuPvp74sM4k/1rOXCxcCD+ua6a/oKpSkunJhkZNyFQVdPcas4kPylkS6A6yb/OIoxtwgtGLpFzBSJYcXt430G/u//uhx/DpxJBVhvJZ456XIXd1wkTVxAJXUW7Ue/vlf2pXirNojVmQ3QQh1LMHbq7S+lMdoGDhlh3EBnSx7cFY7cuMpxuEI8H+aTr5ssjqfBExGqJsESO5wsiOGVMZRRIQN3x42NBqz0yNEB2FoPTfopTFtQnF+UBVNKNsCcISgqeKKju+Jism+BRFdFWQNcslPBXgAgUpEVRSMimbAJpw33FrmKeNdqNQ70FNiDJDiRKtr00BlvLIGeyv7gLjZhvCGZQhFA2Csm3Qhh5tsFUtVyIGuQVDPbKu6vXo8Mq6DgT5zquO4NWd1dRsuP6Er+wqeiIC9qOwor64mT713LgIuSA+X1ehP1eu13WkUw00TKSMRyiiR6br5aD9FLTFYWgQRUZ5pM1SJO4Oi8IjkAXio14WJbBnQ/fSyA55cl2KgFKguSQWYW5MbMQjXVc4c1pvtCIf+C9LLKKRhsgd9McCxiBsbBRcJPQaNbazbpqUHShmiImiBQIcctAYlKVHF0KoReatfMqYq8R6SkNbXyYIw135+enJ9r12oaBLuabWSNvhyy4xGsunTU8T1wZtZzhBN7AK55ESSIHd8ivy0XOOLaCsdTLZJfO8fXjCnMk3kbOZuKi82WxAjM8i9uQeWEct38tBywHTjteWdasYg7IsMYCMKJV2EZ2Qq46ODFbDvxyK5w1wqKiVDIsrrauiARs1qgBPaQohutONYYZzGWgBoCN6OnVGmZ8VwRgbKdjpwoRUMqjjqgBBkqITkB0Bh4wDKDzCeT8QeM6UVqT2vnDmjRo2teDRIcqFRKMElfqlEAiIVGhS4pJUpsDhe14hiA5FbNjfblYkVEjiLupr1DK+r2l3CUbu0kxPQO52OP9YowilTHSngZISgR96tR4lBLnOau/sSF0XEjuaUTfWq0VoS9Pa3U5RxIjDvmKEhSITfSEV2JSTvce9aG9Wg5cDBxY+eO9GPq8HvooA5yIbQhYYPDxmaWGl60F3sy8rKAbuJEc4thidQes7QDgeM0+giq+lBDOUJ7HGSUGxri4MLcCVQbfovG9s2srkk7sOJVgJSSgS6UpD7VpBa55JsWsAMucwOBldakMvprjtUTCw3AqEkrNOqhkIpsRirWIQgy9IF3doGoek1Eu0iiABT1mCZiniMkbevPpsN6VSw2x1aouW8l4jhm00LQS7ZKazjAC88yPWurpjEsD0aMzrCnKZmo7qQi0qu36YsV0zrygyKQui/NQmpMN6s+rUTc1v9T+nkSZTbAcWDscOPWva+3Qf1FSKnDEiwOuBAWA3IV6EKTzjTA1t7BgOCLyhgzrJ+LQKmGWiHpxwMMoi/3sJYsT+CvEM77zUXdLlVo1ekqSyJCR3Cm1RB2UIhoki3CGlOVESZM9MU7dOEMk7uKvGZtn7lFEiwmUlAOVMetNp/1STkjMYE3EcRFAtQliU2winRKwgCb8X1ggUQFY7g1Cw3zyA8CyTGpSQVfoQgLetaEv1ar25j10z7lUCwqEVjxM88dUqAXO5Er25EM78QfkI77yeiYVniLPqUgiTadN7OQmyDyDqVKtzt6nYiEXcQoVQtT3lbUufwNWPrH3lgMXEQfsD2GNvWxGfDFtFv0zlMvQOFF2amBDKo8Z7uzcQtwfGdLl/2oKK0ZjIArZEQDGrZJ8EQV/BOXolU4dAGAE1EoVnJRg9vKcwayioxmilFVWSdzIwfNVp8oRDOAxK+lmt7Q41pZTLMX9C7MBsyZrJGCx7CWjaT2uNvEPQ23yAszxl1yXAbhj4kMhs4YtfQKA03hpdpydW4Zufe2rb77pejTOqVjm5QwDI42bGl/yRTprOh5138TjHpx57SePD/oqpOMqAZfLFeLwiMpVAs5nc6KP4MN0w7Cio10pfnKlZ06QzWk5sJ448GLWnNZT/9dcX1CissOIfSxsfOEAHeg/MoZmNdXGypU14IV5Axdm4MNeetUPdboGrJJu9C5UmSzDt6NmTeoHjQSQzz99lzpG+VO8VZ5KBt9ZqDlfvP1OWrztTTfmsEQWI68WJ9iCDIuL83if7i6WYBugksElJtAi1s4idkvrxqhYJWBSBFzMREjXgBWQSE/mCDyn2o6FgIAl6JQXbOjr/mfvuqWYRx2NSI6jkUwz9LK4xj5976TaUwdFxOU3rffk1db1qkWXM526qhdK5dVQnSynS07WgLGu5tilwOFoUdncXK9hkiXpwhndu2QA2NSb0GXuuJCHYKoyMXuxHLjoOGABeI29cs6vxUWn2PGCC0atOjWzAPp6QavVrJcrDc59FbFKzmUQ3DDjG3uCGTX9mhkUUYGSzuhnHgHVFCCvZ86Uk9h5HxJFMBIb3bqHAy98iLZyHE8nREG20BXKhmCxgMIKmv3Ajbb0RQiTswHTcrSQIVKRWp64FBQRNgpRLPFFYlCQXani8tl57vDURz9xey5fuOHmG4fhBRjZaqfkvB9vvhE2vHQBx5imOVmUxXsznBOoCJBPU2ZK0zDwIiUN66BpCV+gTiufktNzIY7Xw6nGABC9JEVeCDTLQRkYz9XxWIa8vzEvfG616mlsvVzfLBBzG6QN6kvlLzYYAqSwsmGZLS+mQqGdIBwniIQOt4UfsCgrGnqv0ZI+4t2aznN+MhvFMumwRQH06+bMKKLkj2qQv3FUlgmgUjgvERssBy4+Dtiv/lp654x/ADByG+JZI0RRKhhzdGwyny9sHertL2Sfeu6QKDrxCh1mOClYtolwYGtYdwJW6oJJxGUWNaXHskrJQ1FnB005UZiMCNWKFpLhPAWabSICOi3Ryy64zkStHTZnN/Uq7KQZtWucjeQGbEtq1J3RwYFW4C+U65CaDhoe7joC/K4I5fDB4J9MLEJXvGEDA/E2oOhbbQZ4gW0/bOEVubzUIM89jzwx1XvZofTmY9N1bqkFNyBGreAcXQxb+f6+kmwLgtXpTI7DiZeqLZF7W4F4+mTTVBhkUnL0BZ+MnEboLIonsmYmFW7syaBepoNuuiDuKICqZhuI7ybVTTupAu8rL+cFy27fEi4biaWyjiuzACiQFl8M+kJc1F9q0I8mnXzl6dkH+mhOXqakmFLTZ8BXWL2xr4QOv1JrES/yreNo59m5tF9NMWViauWkK/jF8rOsomeN9ZuZY8FypZZZCtMsCtlgOXDxciD66V68DFhbPWe8kjGfQcx3UrJSyfsTC6B2a8fmUS8MFpZqFc3isqRocnJpNpzyAnnZJQxaUEF5aYmaDD5xQTZERBYZjEfne0Q0i7BymC4B86YKDhNblWJW9sgSkMKNiRZUtRjmzT7gVLMllrLiJYqH4l9Rg6FconLEEDeSJ3p04h/aMk6gUGiT7fDU/FyQr2V6JmbnmXMAfAb7pNrZWrvhpvM52c9LVZhnI20j20kwlQjkiwI2ake8O5teLDUCmK8SMM8oAUnSE7fNREFyIzyLmK69RLKWg4nk/UmK4FlUo6Scg0Btp/y8qKrpjXlbpjB94oYkiM6iNgh91U9IH0y6H8pcRCdzLT/DNMWoaYQfJqhaQjnElY8NlgMXLwd0RLh4+7/mei4n85jx0AOAjVflyfExTs294rI9aAQXFhbxPhQFHdRxL4VgVuhGRvzcHcc+9o+Pzyw5uUL3hRr5AFxBnXhpkPXddrNVKpkBXEd31UwK/skaMGM8eaQrCnsx+MWdXPk3wgppJe6iuN2QYrmcC2/2Hxl3mhWvXX/24CEka+UlrRFZnJ+HpT1dReWc8cUWtANjxSzqaHLJE/UzoUpjbimIB2kWhpM14IgGJL7lSdBKOtfBva7cqztomEOv22z2MmvA2jtj2cYysbJzRY+Fm+azIt3eWg5cRBywALzGXraY6TDKieDm1luCMzPTk067fuklXi6TxRv+Us0gT+T3kLwUSGOutVAPP/O5L3z8U5966rnDaAipQwZLeapDofzRz3nliCFZ0BQIRiziXCuaK6gAZRrW8RpkBLx0iGcbkpBqwA/R35D9QjSKblNWjgWEZc0YW2Pp6uFZZ3yuUsx47crck888BzREKB2C0s7szBzZOc9Da08ZhTAHb0mLyhrUyuLWkRumECILE6EGjkIisVRCvSwhPr5Quqkp6+aq/YGTBCYcLApXyjX4QzrrF+rRMy8mghI63qom6FeOuFYQJdo/lgMXLQfsL2GtvXqMjYz4KHZXXppTCKvlxbzvjZSc7hIDYhrHCARMsrgyPgJyKEjrgb/YYJXXZV1zfJao0ZGe2HXG0Jfh2wBQCZ55mG2z6VZsjHHuHSnLjbaWZUZPlgwD2bGTY/nU00PuhDojddJ7A6tm1D+xC9zxSOoXXbF43SDAATEeMoX2HV2ohJltQ32p+uLBIxPwQcRb0XDLEvLC3DyOqHFMTXY+aoMNqEhzCv+yCCpHJEm9cWA1mxNBEIu7uqKkFRnijGvzr0xflJEr6WcfMD1dqpR5lbCIvWINOc/KKRai6QpxKXziLESYaYPlgOWA4cDLMORaTp87DuhIyIhmdKdeOjU2UfGCYLCnwODf39PtpdOTswvgQzTqmZExcDO1MHV8DjnSLXV37T82BfCQR4ZCKozqlFVYjZ47ck9RU0SYGZQ554BQKnULFQKEcou+WP6YA+rFE5aLJyx17wWWAqaKwpJlRTilQQ9VUkaKmeofe/Z4mMrt3b1tpDu7UKkdr6pJGg/lhzBndlH39nZTCiIwiYLaRsuooGUqY7ADB8cGgJMjjTlKgR1KuM0qYmsk7VwsQR1xsAWL+Q0c41wKPaACfYZ+kaKrfF01ujza6P3FwinbT8uB03Bg+Sdxmgw2eZVxgKFOXhoaWlm2PHxkLON5mwZ6sRsa7O1BJh6bnBP8wJAIKdKMjOJqIuWAu4RatfHEcwcTTOrsG2Min/P9hfDZRxS3AwAjOJZKJQiWwB+eyfGAcjAfxOjJV7WGOuJQm6cXINBURR6RgHWOwR8FYHQCjz93OPCz1+7ZftmODW4q+9CT08oiaVoAeI42erpLSMO0bnb9BoCKAi8L7kaNLXBLZjIQqBkABqdZ7kXj2jJ7fqNJhsmw7i6GGaZX+HxmLlKrNUTdgC6hLd3nS4lxGS9A+SPamhMl4JghmiXKFSfav5YDFxcHXmA4u7iYsRZ621YrYBSheJDg+Lzp2YwfDnUXGf278lkGwKn5imifTWBYJA5IoJaeWqwAEsVSfhzH0QbsTBYg2ih92Y57/tFXqUpOKWbsZqMsS4kQmXwRVZDlyticwg7Z8ThjR2HViJ6ca6R9S0rIbeeN4qWkGi00twLArge7joxPAp/bN6Z2bBpK5fJP7T9MZWAwum2y4ciJJgrZqDZaZ5cqKaZBl83WUrOcq6sUSAvNpqx7KgBzG2FzAjkdOSX3ugi8Fz4wXHxlM//gBEIYKJMP2CgLDDzSNQV5KdFmX+m5sFr+xoH7ZUbGifav5cDFxIHOgeti6vea7auf8lQkY+zCiHd2oYLpy8bBXl7kZbt24hLr8PiEDmuc60MvGQq5BeGe3n8kn8uCMdW2++zkiqGQ5VU5ef5lkEcQlnxPBm7QDYkTCRhNJiK6nInb8WVM+alGDXuoHJMGnB3yhIHdS7Nllv1VycuL6D0t2Wb3EblFTkX8fWYGk92+UmFzl7P30p20PjY5AyQYeBa3lFCAxXV/l9RPI2igzWmJgqKQnUqlZfNy7J+rZQRjnHYcPz4F7o6MjFAqxxbf0JyqAMFMlRIklirXZoi0xyuJ540wc8KRJ85E2cTMd41jkZjFYcLG1443wgfeiu34qWchVNDxvldWb+8tBy4KDtjfwFp8zSJI8B//CHPzS17QHOrG/73TWypyPvpipSnQ4AY+42I8DrKIOsWaZ63SVSxUWy5aarIoTovNEzhBbiO7nBbMzjmfcAmCyjIMsynMrQSPoxDv78HaDDjj+F5yQS0fsZQy9AnlJxMap5indIpMohvVikGFoxMzhWxq15Yh8H6ovxsB7vDRYxVzPAPgUalSvRwLofhOaYyg0UirUEvbQkBHSPTMz3MYcEf2NRsVNspih+GueQlxV4zLybDekDVyNjnjhtJzsRwXVbyUMR8TlRdCoHAU4yaKYVWevHiTyV4sBy4mDthv/1p727FFi+LB8ckZL2hvGe5n4W2wz2Nj0uxixWztiTahmnHTqYTO1Ow8Obdt2tR2/SMTIvnJu0c6iSVfUFjV2+eVI2bgNbugkODrnOIUqC3PsqwoSktyBWxgxggalTu+mpE8STIi+kogPA21IsGaZWDxikhgCvLovoPpoH7Vzo08GxnMsGQ+PT09MSPCMRgys1hHdO3t7SUzGSiFERZXFoHhodkFfIJAmwBw52HA0lLckySDJK6fIC9Qv1SFgs/8ie7Ld8l1lsq4yUrljE27ZjAvLeq5ee8ncgHoPUXqiXnsneXAuuaAjk7ruovrsnOuYCWgMjk9g6Z0w2Aeqa6/2/HTmYVKnRPsJRgEYCjkHc+XnYVypbersOfSS/x0/uCxSQMqJhsVsYgn+c3t+b0wUIvbZdoC91GHA8AYYZEgWKcYGw/KCPDZLNncerOddIgR3+Q61dBtCmonImEeLA/bcIaAC47H9x3ya/NXbBkkY851tm3ekE17h45MCj0pZ2ZugdyFroLJLsxIy8kPYdBsQBq3XBMxnRp0XxKZOw8DNiRoBcJ/6eSaD/T71CFvWFWpiaNQ+LO4VOblIhYjASvHdNZi3tipa7CplgMXOQdO++u6yPmyarufDGcMeeWms1ipd+XzPUWhFyG4q7u31gyWljC6kiA4YQyRpvDV0QpHB/su2bYZbfP+AwcjZDAgEcoBhxISUNHb83E1g7J861hbxH8F69nd3d3SdNKYSJDRpAAVNIpitNCsEAuJ4i3jhSVgCsvqN1pROhm2UK0TFrAYn5zp9puXbezSlO2bN+Yz/rP7nxMTMJ/V9EU2/OrJxBDDJ5tmDhC08P9M+ZBzjARmTggGXxWAtReC5VAfMRf8jWMnFFuTNx090V7KkVy8HYzQKsJuZ6lSYyLHWYSwznCs452ear5kuEBeGywHLl4O2B/AGnv3jOnq4JABcWJKBMTeni45J8AIfYMjIzhOnpqeYfUS4ZJx0OdoA6x/j01g7rR5dGhkgKNv/cPHJxgzIzgRwIu+BhI9/yEWjJxqRVTQHKO7PLhLbJkI8DaTyyOYshNYUQ2p/vlALa6IvyaKgh1P14IPTxwElVPbhns2FB1OegBDtm0e5ZyA5/bvR7zmdmZ+jjkBOKpluaYxeMM6uhkpsckklBn0URoUX0+QgLXV9QO88sWgTxHkctMReDvIu3yvKhXJ0MT2IAhJWf4+SWZ4YZjSUdBGLQcsB5QDFoDX2DeBI2MFhAAuxxmbmE+lswN9PQJUBrYGBodbYTg5OUmv4oFPrFyOHDmCX6eNG0b7e+XceHbWzs45DQ6iJeDyEdUqxU830J43DnWuAa9sJB631cGySMBmiy00mo6uzL7ivqMr9FG6+eCjRwpd/Vhg5YzCnQw7tg9zgN6xY8dqbZGYZxcX2crb09tP/RTgykQHWZnDCrVy3VDT2ZByGEU6ibqSnTih7EToziJrM37CKAFnuNe3wEZthODFinij5CxC9WumfWTuonlWdJmcNlgOWA4oB074aVmmrBUOyFAXilvjMJ0vFvMAsA52g0VOtq1NLWGGldaF3cDxWCqemF3006nRnhzwMtCdZ9ycWWxhdSQm0JSlvPw5taBjnp2zC03pdw65sio7dcIsnqpBfzqgH56HbOZhAVUScjgU4cCkdqjuMKCxY2TXmpZPUsJciiTtjcAmxzJik+v54Ouzh/YXu7M9TEDwEd1yWL7c0evk/Oz0Unmx5bD8u1Btp1Nhf95jqxPnK6GXDhGAcQui23/dSE0vdJLBwEjg+mRb4vxEzhpMBRRU8RwvIuKxWnqgxthSZM0GYTIs1X4TpVNMUPQ9onpBT1DHX7bMWjg5KkjH5zLz/TRM4rDI6L1Sz3JM2WEqXbOcsYRbDrxUDugQ9lJrseVfTg60281UmnHNeXYSX1K5rVs2q50Ra8CXDXi9buXQXH0OglKMlWHD8VkQPjC5tLCwdN22vl7HuWS0F/n5/n1HnJLRW8uBwSImstnnfEsnMt42ZVGWc9vLjnNoZi5TLPVl2sChHESYWO84nFafxdc1o3x3Nky5rYmZ+VRGDM/CZoPTkEFQQgAmukGrhu1PUKu3OGyRqQZgyIm1raYgaN3NHl9qzjqpGXQAx594bv8jb3jbrS0n1RVUewKnx3H2bL3ULfR95eE5Dq6dWPQyYXUkW0MHLfRQfz7LycpLNdkhXA8a+BYDZ5RHvksJDlxOw9snD8+62cKO/nym5RTl/AY3xQnErmzOCc7w6AjpzSoN9IK3xheFjzghwS+K0ZXwrvLs5uouNOrVesuF+eVKHZ1BX183/BKE5hW3WADgbWXN8giYzQuVj8z0eE+6mm8xeJW+eUvWy8EBC8AvB5fPbRuuHBAvWtO5cqMiRkxymF/YFvwaZXdrUGFHDQOiGTpdUa42nEozRAXdV+AIw+amwW5UzkcmZskjYiIii1olyQB7bik9qTaEWmRuo7oFICtNWabmbKKU2QAFMSpfGjogRWR4JHofCVhPRAAMSAoDztmlarTxXFMZ+t1OZVNU+NzxykNPHRWjKcDScabLtVJXH91chAtBc3iwlxpxHCnnH7eDTOhsHB1uBu7RmUWUyLNLDT9sD7CeDjoY31tttkab84ClMcMZWo/2zhjTNuIC804mCH1ckeUdWgZ6pAH+y6SGd7AOgkAvcx/pmnTK9Ii5EdO+DIdXhKEs/vKFbMMM2U/OI4J0XfxiRcHgrJZOrmTQyuJM9q/lwEXGgfM94l5k7HxZustmD9GSBs7s7CxeIIaHh2lWTa5GhnqBlompKcEMExgUZ2cdxBRccKil7rYtm7HuPXToEM9lyDR+qfjL0rIZJU2x83fx8OQl1DE06w7aYr5w6m+hoQajHjLjoCqiCBr5ZyjF6kfwUAZ8uQdk73zgoY/8wyf/5o7HEUzLbae/mKMw4uyhxw841WDHlj00JMuVzEMEZp3duzc1g/qBQ4dIm56dR9s80D8kDXHigmB/BsBWarX1hEGiOzegLMguTsQ4DBjjNgM9LP8m+bTYerzSZXieyelpGXJeZLXe5L0wz0s2a8Hh0wTeg2jx+dhgOXAxc+DUQ9/FzJHV3HcdsBj70enVG87iwlzWd4cGiwx06iRqoE+2Es3OLwBxmplH45ML7UZjsL/XmB+7m0ZwV+RiqAWmiYGROW/OGHW9TF2nLbpAy9V6DdDKyxL2aYM5kTDQU99Fcjaju4jR1ACQ0z3XbRnZi5THnn3u9sef++uv3nsIBMa2jDz1Wg7J+NGnAd5dl1xGHsENjqcwLqW37RjG0Orw4cOLSMnlJepnS1QErQBwKgUPG22xglafEQArpQnKLuJs+KIUlsB5PEgbqoTIGIDjv6bMGrzoVyhSS5xEP93H9AzQZTsZD82xSPAhh0oFjvFUXlL0PdSoubcXywHLgZgD9ocRc2KN/AW7UGyCXouVsFZZ6ukqdhcEa9ldJAMiitBshvP7yjjmNdpCrsePH8fcd8vGUaAFS9XeLqeU8Wr1xsy8ml9RTgVoiZz34GLFLYirhwEjMGHIQ/s6ZC+rLGM6CjmWGiNZOUoLIlGdsiZFtjrTTWqYr7dzW3cdqAT/468+D4tQEuQzOaofOzyWavu7tu0iPzlZmISFKIkHepy+nm5ccBw6jhEvIm86jTYcNogS2cmkEO88jiMkgcr5xAZGESH8mZ8XahDTpRNJiIE3/ps8WMuRWF2s35KQ5V2O/i0WuS6W8bTGqZEo+83GpGTyt/ILpVzkqkFv4zv713Lg4uNA8mO4+Lq+NnvMmK6+kadn5pr12shgnywAM/6ZUQ9EYlcSCD01JxDSMlB96MixtBfu2LKRPIhuqGRHh/qBjUNHFqWUEXBEbjMh+qM35+fqp8V4GC9YCI4IuCtH6ajRaHQWj/+uW68h0uraMZZQojjngoQqUCmrrRHVjVaY6h6qpgtfvfuBbzw6gaEUMIuP4gOHxoqFwo5NIpbBoriPLYTjrVs20fdHHpnFSjxviGEuIBZhIgGzTBwtbaLpNlrnqGw0BcC/x8ICvdA9SEp48ojb03RNM66Nq+gDCCf2RO7MF4a3w9dncWmJlMVqBV+evFAdU0hhUsIV5YHkt8FywHLgJA5YAD6JJas7AQBG9cqwODk102rWR1A6M8aZEY4LQ95gfx8ueY+NGweBxhTo6HHODHa2bhIJ2EPIYwfO5lFg45kDh5FizNENmKrGAs757r5RG2OlXCnXsplUIScbmME7rsvfRdMdCAH/9EhgdgwndKn6lytPQ3C4BZCnUKfTl1qzNTuzuPfK63p6B/7p059/Zqxedpynjrdnqq3e3p4N/YK+gLYBBsRwUWfvumRnLpP65oMPIMV2F7tJgUvY8UJCGrU+ByKZFWuhyECOTlTgnpKH9hUtugqCEYUq9sYTmoTs9RA54Tsi0GwWCFBmyMI6OhVWCFBLxG8v0sQnc7sODsA9YaANlgMXOQfsz2CNfQEY9kS5igk0y4+tRm9JNLSEdoC8J9DS210KvPTMAkZIErASmp2bTzntwW6kRlmtBIE2oHtFNT02zrjZlI21YJF8E8671S6zBzY7mTkEdlVAF+M1rZ8mCElGzI0soXQo50qRFHMKkNv1kbo4gIeOS3/bQbrd/p5379m2ecuzB4794+fvXHCcbzx9KD8yWuoRay4AmKsbwBUgFls0Z+PQSDad3r//uXTaL+SyVCoALOcOonBOsVeGXVMGfcXSisQIWA3Kck8vYB2zBEpFG1/JZPKdvl+aY81cBWklSIfgj+IrByVzizUBNgR1vliyWI6OwGNbsOQlZnLK9VSMOFWalLPBcuCi4gA/EBvWEgcY93LZFPLgkWNj+Wx648ggAx5vkWPgWJVjrNwwOsKRDMfGJxkouZ0sy+pm1gu2DrC6KYNndal99Z4dwMnBo2PgTDoDeIm7CR4pxpxXduDekfoLOX98cgIT7r6eXmle1oA7x3kZvBmj+XT3drXaDSTgRuDIom+rlc3nGOllSy4V+bGjDJO5Ua0VG7UBx/mJH/m2lJ+9+8n9X346/Mazh5dS/o2vuVZcPVOisSTbkAKvVZN9xlfu6Q4b1WI+PTc7vXl4BIQu1508HjrgDGcSt9jOaxAkCACbRpsGDXuMLTdRLI/m5+dHR0eJxxZaUQYFKnOzhi8xUsZvJ+6Ki59uXhznNqdS5Yqs/i6Vq0xEenq66/WQAybl9ZnCTFBQ1UTlZBeXcjCuyP61HLiIOWB/DGvs5aNtZlhDgptbLAMkAz0lBvq2cdMENoAffV0FYGJ6fomOMWoenwxqrXZPKQ9Oq3K0K+cP9fqocDnIYYmNPCabSsC+6rLPM0ugSrrQFDEUqycGcrqgn5NbRkoGAHXdV56aPmAnZSYMVCTiO4E6mUyww2m0mC42ne0l5zve9u34//qTv/vYgdmFZ8aOD4/0FD3Oq6i6AZyh25gsS2/hy0BXvpjze7tyLj5JMMVKOeIiTCRg8aONyC4/EgMbykDTYHRBAmbRNzYH63yyvuL6esxWYDomDGnL62PxGxbVmsxMYJRsn4Z7ZoplXqgoOwix1ZzEzZs2f3llfGywHLiYOWABeO29fYYtxMHJ6Vl8NG4YRqgTDS3CK3gCAA8PDTAUHh2TU/ZwB3jo2DjXjcMD4BxYxSodltL9BSdXyJfrreOTZoVTRkWR8xTMXh6OINQCXbqICKUyGndQQFQH6GwaSynR9IrARTAYyIWPZGeA9+R8Q+JUgePm1vx0t3Fl9Z5vv/Smm27af/gILkg2b+MfynqaKDvtmvEpITIa3/4uziXcONgoz6UCtPRFdNBoBFjG5FHWS1G/rgFHkxeDJ4YOEuRmaWmJuUunEZY+XfdXnRLhV5yZXLUqS/TYljdbAYp+vFHKazGTGHkxMpVRiXjdc8V20HLg7DhgAfjs+LUacjOmYSXEZl9GOtn4y2CHAjcQLS4D31B/l+ulxqfnIJWBcGx8wvdSWzeOiKBp8ANfgEjDGzZsaLve8bEJgS5ZvTQj5fnvHpCmLaG8ZYzGONkQZRrGxoeb5XtJzOcFgOuVqhJPV1FsSi6TDc25BAVgNmixy6q+tKFHJiJdjvPOt7x2tLfr4L6nenLZIVEP4KCyJgKabNjFz7PUAyt2bt3Qqi216oujfd0UJDSNRj4thyFFVtDKoKhR0wMFYPTPADA4ZMqt6wviryyHG32A8F9eY7GIkVqrWqthf4XhWq3RKuTk2wh/4a1BXfNujIXBMndepu/acoM2ZjmwOjlgAXh1vpfTUqVyYbkijoeKhVwpIzlZiQMGGPJA2e4uJ5PPLy5VyEnK8ckZZMRNo5z8E4EWgMtb37FjJ06LDx49JuKjQJpY1RjDmtM2fQ4f0CgADIaVSiWqNZtKV47Kep83RmbVajkCYKg3xzLEWAgYChQg/qIGbYVBpigc4QRCPEK/YrPz7ltuvGJk4LVXXsnmK3ng4gMLR9NpdNCyAkyplnPd3stG+rsLaXfr6Ajr6JV6I42nj4B9wJKh3hLrNuUd/CToSrkC8NzcHEryri6ZJazLIF+PEzpGgnyvWPslwttB+8LhWnWz7xzOMIMR9D2xzAkV2BvLAcuBmANmjIlv7N/VzwHAEjl3crqKMDLUP8D7U1QwEolIdjieKHX1TE1NzSw4WD6PTUxhkTo6PMiQSE6GTz9s+W56y5YtgXP3wUNHQueGyFhGK3pZWECLADAdKSAxrQhKqEkkmjPPOfIvoQ7lZ5qTGTSDiKoSpV8o01mvzXXnONqI4ynwPVJxUt/95r3XX773ih1OBoiWlfKM0Rj4DdaADQB3pZzRgXy7sVRdKOczHrbVKbOPBk6ii4ZCdXEM88QFSizbJSQbCbj7YhCAky5HLwJVhKwBswrgNxqtSlV0MOmsmQ+SVd/KCsE3qaIj0vG2O1Jt1HLg4uCABeA19p6RcwGbsYlJ9MzDQ4MGRKIuBO0m23zBCHw+Ezk2PpXzB2UPUiozOiIOoSjIeIfsx/6bjRtzAPPx42OkiE9H9WP8MjDDTCDYtqLeJcXPBsO1kcGFOKHvhGBcQUe+oHmI4MtATw75TzCLtaRj4UwnmhhMlYpGSVr1nWa+lRvxM33bRSOdB3w5g0msm50aO6/wtkFNooxms1G4aeNIo7iEZZqc+WMCFOmGmpYeRAxpy4+I6Qqnwz5gz+8Fhy6OANeXv3GsXOAEFSvoer2ysCBeX3q7euBDZE0QAXX8TpPbi4NTtpeWA2fCAQvAZ8Kls8rDIKXoIJrLOHTG4zSGcUYluUZoEj0AHCTlFEUEq8CRwBmbr7T9/EB3lpGfVxi2266fbnHCDxDD8mc+i+3w+Oxib19ftel053MDOQEhMKgWOCWP43YdFkrzbnW2Uqsix7QaLtbIoA+CizloyPioFBGHYEZOzJegahkfZRogNrFk4cBek09zm2hSrKOG+IGpBK1ure1mgmbRFWKMjTFtL3dZe0rX8KuEeVQl8NXVRopTFEJxzqyhZTglmY3+PAg9cB2MpadOu5Xy3VLA8UfGalwyQaps1WoB2x6MgpFgcm5Dyf2pH/2BxlJl96Yu8nIOIiRRIY4nffoWcDowb4NdXioAcyMBEy5qrjY5AbdV8OUtkJVEYcY60sDCaumRvGj5TvIHbgj/+a6EYq2W97xq3Z/Dsi0MC3nRHsAFGJAsGURfD6kkeW9yY4PlgOWABeBz+x1gkGJ7BlcCw42OODqCnTT6RIhFZmMATC5STN64hpNpY+GSPR/5A/PNJSe7a6S30ERTKrpRFuFSKAANbIz0pHtK2SePTbpdA162a6i3n2OPWLOrUL1Qwdi4uCvXNZSam2vlHj/cft0IJjSLjKXSeFDFS6RTHAaj5st1fFOIx4s2tsChn82B3xRmlPVR5wYMum0n1c2BubkMFk5I1FJ7fWk+y0K0AdqYEQaWTH/DMMB1BUazY7N1v7a0a0BEc8dDQywnH6Dy5ZbzB2WJ1vXQF48WnFS7vtguTgQCxpxfmC/mGex9lnk9r2mOcmL5uuS7mJ356eJQPsd3GifQDXxLQpHPsYNguOEt+7RE4pWDbAk0ZA5FkrXhK3pTXm+3/hiy8kTOiGIWlGV60PLnqc5F0hPRm7fLlmunWQvSmfGGU3NzTnVuJ+vuUiUZ4IDqtkUO1+7LkzUb+EqYk4CZ6gjyYupMp3hTKVd8mDHX6c2mFgubnjpS5pth8Fe/BXGH5aXLt0JC9N2WCFFNTdI0i71aDlxUHIh/GxdVp1dDZw0aCSECiQz2DP7J1aSYodwMU9ya1wSQSAhwojix0MCNxFAXJsJ1QUEPS2cR6AigB1uBm836dKWBoIwsO9zPQcARBjGIIqWBQoDQzs2DdSf93MQ8x86LDIoOEXzHTKnQo2NlT5HDligg2lhfznioiILbZe9NvSVQCd5Ds3gflIZpXraDOtl8SUboOHRE6SwkUlJgrNriHN1GUZTBAlRkM7RpURLETIpHAFvaDVpuGn//+ICONL9SoikdNwXZKSx1ohJ1XORXWMkDphyYeZu6kVTBD+ki+XgkGG/AkVLEoT4X0hB10kKLFGU0f1IhdXuSSlZZAiaqJeW+itmXm864bXDIgLdoqYWm6N1JTPKt5RCpQei02QSsAEwf1V4P1qU9txGmF5viKSyFX7IVnaX/yefER5p8Ypq9sxy4uDigk/6Lq8/nubdmeJc24rHLjOfLGrkTmwccEojSwVqHsM44JWStU5IQQVLAyuT0VLttTgJGqpPyARBc42j4rAAAzrDYODsxM5fJHnPD+o6tI+Y1s/tIxBeW7ShQbrc3b7u0fXDfgfG5Zqo/E3a30oI0i7VMKYfJUugFNTeFJhtgxcKaTokNk9tsZdKpMJ9dbIYerqEyYnWj2lpkxqBRF82uLCdDU9Itii4Hhmn6AZHq3jkTWVMtZ4hijOSGBXADJ5E4yGgA2kwPlC+aSTYhSTNqkKw+MRBUkyyaflLVZ5pA5dQAwcnqLyWjyo3NG65EeMb8I2nxTKteR/nYyR1U2uyHVlaso57ZrlgOnHcOrJywnvcG13UDCJcAZPwB7fQjfY4QKUElM2YLFp0KqUjmxYBjRFhZxOGGyB/G+KjRlqXH2dlZPBgPDRkT4djPn3ku8tnoqGhLJ6YXDh0Zazcr2zYNymtuNwBePjhj5m8Q+jt2XVFve08+d6TiOBM1b5pq2ZaTc5aQJgHeFFplVMUBICwSD2JisVBvVAWOfOfh/cf+5vZnxllUBkqNOy0ESS+NKCgUc/ywRE4ORkCiKsAUAGYDj9pYIU+Znp5cQPjAMQk80CpPAOAYDo1GM7LqwhsiRQxfuZq/nfhpWkhewina60hCUcDUg/7CzJVFcJIVhHpChB4X0VFuXUVFX3FSkK+T+YpyxQkJRnzz83MsY7BgYZ7Yi+WA5cAZcUCEIRvOIQfAKgbrZNTSoQrI0Uj0IB7OyYYSL3kalZKnBnEFOXjIR1OoF51odqYMejX7SwWBGh5i/psWu14FJ+B1iGN/0unZpXoQzLlBfcOgWWc1G18F1HHf2PZYKR0d6a3WW0cn5wHRbF6gFNhkHD36XGtnr7N7gDXlbKtWSefSYJBRlQf5YhfZHj8891efvuPhY1V/YOt3XJ1hLzK6ZznHgM7IGikysEjMpwyucaaMsI4rykI2y9ox5UTQXJEbXogCWZ7mkOvrQYONLt1yS04RbU0BLkjtNEuEU5DRMnfCoZR/CQFlOwAs/GWGwQ8leanUCcExAOv84CW0s1aLyovA4q9QCIPK/Owcr/Ai9Ai2Vl+epXt1cMAC8Ll/DwYalodrHbej0Tt6ZjCVuLiPFElXAjF9KuirSVw7QESqEIOYw8cXwIXhwX7JFImEsjaaNoZAKL5xr1gq5qdxlVVvFbLeoCzptow/QFMxwNYSS6FC1imks+Sfo8Ga89iBpQPHJ79x9/1H9u+75drd/98PfOe23rSf626HbbTeCOALtVouXzgy1/7zj3/pzoeeXnC6Pv3FL9969bcVDJkAIcrfZqsp5+GIlwadNwiNnYGlY7pQASvDkD1I+v1zOdHI9H5FGXKSLgi3KN4ORXQHec1V6jSirSZyJ/uaOI/WKLU7uAYhIrJrSOpP6ogenOoPlSQScOdzympgDsFUYP07go77y9/oawxnZG1CvIpxFGMYTi4tzLFaXsipfVtHARu1HLAcOD0HLACfnjcv9gmDlI5TjOBEzDXB1Bh6lysXn4hRiCLRcm+cKn9VQmy4IqTuP3Q0n06N9PdG2cVQSsRgxE7VQgM3PaXiFFuVHH+grxv/iq0AEBUYksPsjWaWF99fdLYN943PTP393945O3X0oacPVhptbI8Rmu995Oncx77wfe966/Y+N+OmsiJneq6fm3Ocv7v9vruePLLzmptabefYwWfvuXf/W1+xg1XQWsMpIs4i0kJORJni3YloaIRksBLo0sPblwFVOtoZooJZXG5y7kTTeNv0gV3WohMQlC5jqkxLNVklNicmGSyXFfiIjM46ZQpC5hNoOuH58o30I14DToikrNbKIxGNmROdXtxfrmutx2RJfpnnnb3BHZsbtqvlCmzJ5c0csPOxjVsOWA6cngMWgE/Pmxf1hJFdx3fGI+KyX5YgFqSxu2Udv6PKBZiNM4nlxhSPTbEILaQCUwoY5IOXKyTHnkJsbWScAuKg0fe9AHthL8Uo2JXjKfJbqreHzTugFNKK8Y8PbPIE66WWU0o5W/sKk4dn7v7q/pTb6O3uff3rXvGmW14PQP/xn/3tP376C/2Dg9/3jhu6hfQUni7YFfTQ/trff+YrmZ7hd737bccOHrlj8tmv3fX1V12/o5fvEa4tzMKxdJTdQrHQaThg8C5GRGCLeQAApupirMnSPgfm0GOTLeLEMpsU4QTtMEOOq034ZcRguTNwiENOo+M2j6WKU6OGeWwup3tOJ+ADQeroyJTEeYQQz9WoqZcrvBhi+m7M19vJsts6RPNRT6e9jB9/Jy8GLtg+Wg68ZA5YAH7JLDyxAgCC4UmQhKFaZDUj+zJE4fEisjhSmDFiLhna7BQiN1tcjS8J4JFdPZxrDqIoZBszZ9Cpyq7TwPmjv/zYV+59qJUd3LN1gwGjMGzU3VxacQesyrlO1UG0HXjqyGylUt61fTfVu1hUBW3QhE0jMkNg140nmHrbTZdXJ58teu673vltV19zKehOneWW8753v73dbPzZX/4ttH7gHTcgWiICHa84v/ORP84Vu99625tetVO2MX39nxYPHJh/ev/03l0DxZT4VUYarNWa+bwubZ/IGnNHL7O+c/ToGKQODw9z8N8AtAnJEd+EGSikZcoiAZI2bhh95MDE5OR47tLNrWYjI6p2ABwTqRRP8djMmb1N6STzjwAvYCRSI1IzdtEZNk5FWnqt70yvoG8dpyWlUqXRnJpa2rW9FLDqy/5jz2BMveFlszjIpLqLYg1Y2LuCdXBDvkzdXcV2q4GTk0w+l03b8WQFl+yt5cDzccD+YJ6PO2f7jPFIxmgZ8cEPOT2GBAW8CH1Dh0PjgYq02IumsSwS7SnjGCWQ3Fg9BVdcWSXFrIm9wdSyUIv8Id/70DOf/OznHtu377K917/qjW971TXihzJoNDxAT6RdySwOi10fe6jRnpzbbuSy/mB3D+8YETZSIcreWSETCMumnTfeOPyaa38YGRl7Lh4AOazqMYpesa146803NmtLX779jny+8I43Xz5dc/7ib75YqTWuv/rqd7x+O013O87b3nzLZ7705X/8xKeu+ukP0DqLgvQY9K3Wqrk8HYwC7QkbOgKrpzBKtgzJMyYElKQCnZpoPlEh8xHGGB1vgOzOrZhrqTpAspmeCMISQQKmCnZOUYt+zHOqxzhd61y+amPS+POGWAIW6jUeZVe64XZM3vNWsz4fCvwanT/fPb7znhum8cRmg+WA5cAZc8AC8Bmz6swy4rBQMEKMik4ItcWFbL7oZnKZorglFpmST7bgh2zdBVsYzHyglEfRx6AvcVwtPTvpfPXr93z9zm8cOPjMbW947Wtuft011w7mKI5bBERl2kI6Y3HUNJhyg6zj7dzQ6zXLmVRuyygH8ZljCHy8bcj8QNCKcRKgxxUGxyEYHx2cH8RZfnk/sxTIgQQjaeetr7ws1a7+yV9+7B8+85XBnZc/9cyhex9+uuiH/+r9t/ZnwXOh87Y3vfaLX7vryecOPHmofNXWomwKDpxKrZWXIxSUnBOYIDdGJc0BduxvyedkwE5QEDqpc0XgaSYFpoaIszxaoe/lKc1oDRwP5boc0rOsBT0BMk29ZO6E3tOQGJEArmhz6n36BMIMADONIJEWT3i0vm5gUcLhFT2DB7wyXmIKGzqU8ZyEkbFrwCuYZG8tB56PA+t57Hi+fp+3Z2bIRqTTgcuMXWbza657AKSsB14DYS8lgxpIwKfu+kFK5IYkBZxBMQuMHBx3pmdrE1Mzd2Ga/OC3tmzb9kMf/Kl3vXEn4EZg7EOXK+VCrxm20buSIo4znCDveMNd6VZ5wWs1to3IGbciipsQImgCLCAwfqZSXhZJGK9WToPlOyQYp9UI8e6YF1wcyjq3vea68an5j9/56Ef+6K8Am1zK+6Hves+GrDggrNWbKBy9knPdjTfcec83P/uFL+/6kXfSEAJ0udEo5AuqPo9ajVAvvpOzCNnNJLtWIMWwAfpiEjVXtHgu4IqOFyht1qtkPgGAjSMOsisA6+kOug9YEw0Ay6RDq+y8kqRI3Jm4Im74KSxOAFgRXaoTHoqKm5TOjU8rargYbo0lnXzr2BhN/GLosu2j5cC54oAF4HPFybge5AJBXBZ0SUHWNM4bXXHoD6zivFD8WxB3nGePNg8cmzoyWwtSGLJkULRyml65XJ2dn19aKh86dIjbpUXOmUF0zl3zxnfedttNr9wu4MdWDzCAN8euH0EXOZEmS53SIKFdd73mYN7bNtrrp/KjXTg0FuslDdG+J9wHNtsiPEtZkBenkobgdtCVl4P7QGKO/OvLOu9+6xvmG/7ff+qLnDn/7nfe+pZXbk/jIdlrsZyNNXLL9d/61jff9eCj9z348Nz8O1Mgf9rp6SlI5WloFKiK8dFE4yRxnBS4ALBipz4Dc1fYo2l6Aa/Drgu+GsgUbXWCqaQw8zCswIM1DBY4pE5t2+yMVhqEiog/HdBrKowIW/FH5jVo830fnYFaO5PBqLpNr2IAJvFiA2C+SvLWeA+8Cd8r5thlHsp5VgCwtYJe8TWyt5YDz8sBC8DPy56zf2jELQYotKEM4DJWgQBA2lwFfTObRgV69407d97zwH3feuzg8Um/0NNELEV8MMcPoMtj8ZMxv7q4sGHj6B72CfXt3nvFlTfe1FtCFkTqqjm9OVMxi6IsIWd5g+IiP8YZhkXRi24b7nnvO7/dSxVAayTbSMQWt1Z+2zMHHnGID49wL9mmaddvhRgQg8biYKKFuRYeoASeR7qd77ztdV3Ix07wvm+/Fg0jCurywnyxq9BaXMp2D430Otdce/1dX/rC5754x4+85xacYYDNRmSUUZoQQ7/eyRVSOcWPCAAshBGEehnOzY25CPwJZJKkEjCes2SNF2W0CdAmMNwBq9V6LZFH5S2AlpLJZFuuN4ppzSclLyeIrj62cJa3Y+pbfmxIVq34RQbA8aTFvAUYgtRr+CxMKmQvaq+cnV8PG7ccOBMOWAA+Ey6dRR7GJ4FdsyJLHDMddM4ccwD6Aoz7p5xvfOvxO+5+8JlDx3LdfTt27rrhip1hY6lVbwTtuhyfV8j2dZVK+cyN114h2MNpeTiW0jqFisDP6WE7rbBV44g8AUkD8FQev0sAvJX227e+cjPAnwE60DGb2UDY4uwAPGWK1AhVVJtn2Rl9M9WQrdYSSEu1/aAZZAq655jaLxt2dnzHaxB4ihRwnJmF2mD3QKs8my6mW81KK1249dtf9/QjD33xS1961223DHc55UqrVEAbTSOnQF+pwjjNYACPNJaClqcNEADCsdPF2G1F2Qw6RnExhDJTHYVDdYuhAKzM6ayavAZKO9NOHacJXmSyBtxJotLLtSW7qtf5GvCpuUNqPDsBf/lGuoFYPVgN9GnZZR9YDpyKA/GgfapnNk04oENvLMgKpppPwhyV8AR0TTrHEWjQbOic8fqEi4ipuflHn3z6nm8+uO/g4VS2dMN117zx1tteuZsThUSlLMu08UdEWpH8MKsCK1lObokNVbsu645ynnyu1Wpjs8whRCaX2wrbvq83pmWxssIAq5wtZaFBkLXd5KhCnjWDdsacmyeoFm+prZHacvIZT4RpMrfKHLGgdsZQAsxwRAO6aB5xIuHsUq2nJy+OJznyKKimUn6P4+zucbaPDj00dvhzd97/3W+7wajg1YUw7RDojUFI6jAJUMVhwKHrcdZS/P3TnCa7FjEJID4UQWrTzbB8Djs0qFkzCEkGfHSJFRpa/RZCq5cWLYMxfKakCSLJRlEhhZzxK1JLOaP3jnLIk6hc0MYoDu28WLgZyDUW4kaXD+b7HjOeapjG1UnWDWjRlJKGTOU0IiFuSO/W6hUG+bjiNJbq7GbjGEjTE96pMBDPK+wHw2c2b8kYJazVblq6LQdefg7EA+DL3zJjlri5F6UojSNMYE2KEJMo9CqVirqWPX78+IYNG9BA6oZL1gKRcihl9Jyy+USFHmpASMK7IbUlZV9qtxhW0T66brXa8gspxtxPfOWbn/vqnQOjm9705rds397TZfiHarRo8KzqOOMt5+6Hj3/969/Yf+AAFEJz0ArrtUopn6sszmZTqRuu3/vmW265du8GCKX63lhy1VFNgcDENQHhwpcRz5d+GecQXkp2W+oQL+DDDfXE1i/sIiqRkC75Re08ZdVFvstZSVKJwW19Zipmp6zxXhUhDw2ZlViySSCuMUZh3x3oMWTIG4MwcvpdZgLxnbe++pmnHv3Hbzyy+xU3vGYwHbSqvuzuRV+svRDCGceFneifA2cabXGmMNqNHpxjAznrHv8Z1AnCduw+MgDG/XBfaa4eHlkMWLTmG+JncuAfWno2vojZddiSYwMdZ77aDLz0QE8Jg+582gMvcKLJpiQXV9ImxAxdvouBWUC5c68SxLI0Xm86vSWU5GFlcYmcvH0vCDNeixWDdpiqcyyj11UJ/J4MhuwS0B5EvZU7fUEdCZK49oKwBqoRcsHgNnbmXtoTJYpJhsVePzoPzqks9rcWZgeMs+6110lLseXABeLAhQRgJBMCYImiD+zEqSzoC2hxyxoht4rHoC/MAckUVhNLy/n5+b6+PjID5MAzEdCX4tSpyH1uWMoWoXQql0thtotrx3IjmJwv7xt77BsPPTWycdMN195w8yuv3jnkLATOA/dO3PvQg197+KFWhrlEJgTJAAysrlJ+sTvTlcveeM3eV16z96o9o4MZUReDOFw1chpSdfiOxEfJEyOGjorxVZIFvySQORr0l3eELJeSHB2Z5XY5RNmk+OnydNQUNeTWWjDnsu2jO3bsuGu8/fjhpesHS0WQSKCXq6neFCMaibAebAkbQVjE9gwB3snxymP6BbjITmFkd9xjpcIWmvm2l60HvmIaNVIV69oG01vYtLF2zSNWsflkPAEKpg0YBfFNoCa+HiYi1Z4YaESr5Cq91huJScDBJW3QDSqQbvCfhX0nrON5MXAz4HEjSAe4OMGMXZ5JcSFb/mpNFNI0qW7tBvrA7MRwQvQxLmcuw0rTM/kC80Xw07xFpGBWgE9i8trtt6XccuC8c4Bf0IUJQCxwi/CqYJnAKkgM1oK+kIWnIfIg2hLBCpfM4CtYSyISMNl4BCqTroIv2RCI8V40NzfX29t7TjqG5tVFUcrIAlD4zutf+4rhjVvGpme/9vV7jh4//vG//9s7PvuZrkK+Xa+zjotkNux5O3dect11N+zYuaWrJBZJXUa+XKo6XVmnhLCG1g7KOA6B8RqYYDBby4MW4ERHevKZSy655I6jTx07dqxx1aXSSTolsHWKwBNeIi+aV59CkQHUiYAVofPJBXi/ICj5eaRQ2pknSVFbZdkHLPggIYno7dleqYZZHaX41q0oi70anVOS1vc+4BUd57WizKDvZsqBhb98f3lB9XY7h15qZW57bzlgOfB8HLhgAAzEqmDBKMZwDGqqvAusQi/ujgcHB3t6elTqBX3RPAPSIC5XcurwTU5cDyoqM9oqDJN4rtBXcFfWuCSI4/2UM1RwevduqDsb3vL6K556ev7hhx996qknjh8+wpacS3fvQr599Q03gLtdxWiQomCTgqHTK/Kw4zZENIs8N6Ak5Ygf0Q/LKL9GQy4n0g+MQhuBK2Deiywls8comVUkkbiHKPV547x9sw044J1mlp1nxJk6/vLGAUKKkHYyppKieAAAUyeTM80jMwMDn0ROLtVR/WmjsuZutiEpANMP6ZTYqNOgvF++h9RsWjxtJWv9gX41O96haDVYTWA/OYZ9vHe+1blcfqEsi0cXbDRZ61y29F+sHLiQPxlGTKQHps8E+E+ckY5BjSvoy1ZRUBkhaXFxkXGQCOmMdwQFaYrwiEqAW1IoqC+Rw+pBZYqcm3fKZtt2y2NJE1/HxmtkVg6cdzgE96ZLe67aefP8ws3lcoXNtL19+f68kwdf8TfJ2XkoMJF9GbrMGLY47xQyjviKUriQzUIBrpnltmN4Ozc0v4y10MFateFmM8AkzfIWhO+qfz6RDHrJB7bwosBpXrpwI5Ivn+9l5XIZBWBmMrx95R8gyD9a5xHp8jEAzBHDolc4F0EBmJqoWevTF9UJwKQDPOeitdVbR8fXU77KupafsJiITHzLjYJsCLbBcsBy4Cw4cCEBGDIRcMFdhjCgFDGXFMZxEhnjQF+uqnzWDh0+fHjLli3EVX4iokVYDNb1YxWSEMU0/zm5yqoXu3qMfQ3muBoYcheWnFzR6fadfJ8T9hbEA5Wxb5LlwEBOCAAfsC6WDaTi0dDp6zbQK7mCsFbB04VLJoFoSVq7AfLTaH09WaSvt5pzC/P0hROCZGKhgUhHH7mrVESIZNTW8RpMjbOe+i86kQiARWEvAKyBSqSoKc7BFQrAzOVWVEg2k/MFWolrXf7L29ZpnNTc0QlqIxP/dc7HTOKsq15uZLXHoq4lTBcNjvRdVsPNkcC8+kIh7zkLuKSkM/FPZLX3y9JnObAaOHDBfi+olIFe5FrQF+D8pV/6pbe85S2Ms6QQkGKVO4xuxG+77TZG1be//e2/8Ru/wS1jPemoLm+//fbrr79+aGjoNa95ze/+7u+STmB8xHvUOWGuGWvYOisijug3cdnMjp4Wpyg4vZxmbzxjyNWMUmSoN1vVeln8XGEwxPF/lMPHFCpZ36m0gip6aq8dcvReMev2FNnh22xyMMM5ofRCViInDokCg4VRv9Fg0zEhXtiOe8df9vOQD5ZW2JhlvHBIRtV8oI0/fWARgC8GQjNLsQLAVHFS4BEKEl6RUaZEWuKTcp1NAugOzcbEj5o76UsAmG8gNfJVPJt6117ejjFCXiesEG6YVOULTsD5jufEZbcNlgOWA2fBgQv2k1GNJUMYgxuoycl073nPez70oQ8h1GKf1d/fzyMz2oavfe1rEUEeeeSRX/7lX/7IRz7ye7/3e2TgEbANMAO9jz322Pvf//6f/MmfvPPOO5GYkUtQQZ8FD543a7UhHhCRr4KWbHNEEY10x1IuoMO1sdho11rgLOpX/Fyx+yWPNz7ZjYLoK6Oz6EiNXayfxtLXx3VkJWwuBaioMSf1vRw+k9d4MNt3GJh5j8ylCPQIpT1XQbDlEI3bJPCCeH28dH0onvxPCapxWRWUjYB7gnTbWQok0FvezrkJhiTopLbOhjorhyRuAenOxPUUp/Mr+xa/U/3e8tLJwGkMaY4A8WUrWfx8PbHB9sVy4Hxx4KxV0IyeSK4rBh1GKB2qTiaz8xGQyQBNTsCVSlAdY2alRX71V3+VxD/6oz9C0EGfPDY2Njo6Svyf/umfDh48+KUvfYnbvXv3Tk5O/uIv/uKv/dqvQQbX66677vd///eRiX/iJ37ivvvu+/CHP/yxj30sGdmTZWAGSiU4STmZztOlZNh1agS6HMfQCqrIR/xHADwpp0CiDDl4yiBBBIPnCRQ1zh6xqZacnC9/igHuecqvyketVt1Po3wONm3cyPuamp6GzGYQYtcsY7fpsxJOZ83GIdljxjyJKRdcaAe8GkSn01pB822hIN8WXuvsbH3jUJpvlHHyrPph5kVNdBTz8xir///bOw8A2Yoq/Xee+BIZRMKqCAKCIopLVBRFwIi6soY1rYhhFbN/EyqCCsZVV8QcccVVzJgwrrpkAyISFBCR+MK8men4/536umt6Qs/07emZ96b73NfvTt26Fb+qW1+dU8nmQ2mYAHMs62hIhl8YSUZPDseQWlELu3aSUJJCcSP2Ugl5GxKYLOyV5XpK6R+KkCLl4o62A7UzMy/WsFq6WhzNj4IJTvxyBByBNhGY2cFd0JsYlDZUI7VyL1FgTr80TzRSXLwVNeKY9lRkTBOGgdBou2lYd9llF2xYRATd4h7WvOaaa3bfffc4rPuYxzwG9xdffDEh8ApKvvPOO3mLQnv//fe//PLLUV8TGtERoNKDQWIxs7oYRZ4zkfNYCqDQrMzmV8QuLJF02bXK5qbQOodVGhiRe/nZehzabv0w8wuNmN4iKWORuAjmSe1yv6IXQncklJR4jl4UaZjivADcjFRRItQKStCgCCLmDAezH3GMparZnF4ocSPmbgujiouQZycJG9Xqrkc6Z1xbmyW1Ns3S63KZur39muFcrbRu2AppJdfmrQ1jT0/vI9DJ90KrhJAK1cWmUO1jK7RooWg6IU4c4EXUCBkjXmAJoxMaA2k4YxUpLrWI6Oabb8aGQ4Huc5/74Bj65BVkzP3666+noYd69913X5TVBEs4D3nIQ5CPcUZotJhqHHHz9re//R73uAch4PLII49slchW9pAETUxobkg9g5BNP8tP8Gfts+3bYCOf6OTCTpEpNmhCUGLYmO0K2Y6DQeHwQ0Ubf8zYmouhWqVlq7VnIw2EVMa9axOlMh2fbMYU0dM7LDwxN9qEfrpulKx6Y8qSzWie98IxZaoqJIeRuFUJRfzdHY4lqaST6FSXMMQKrzQQKTbdjVQhbz33KfF3elVliIVlR7kcB2lRn8t333pjIVVU/3LrSbynxBHYyhHohIBpd2gK4VFJBhgQOufJJ7T61re+FaGW1ooL3oUjZYZQL7nkEvwSJhppJGA1ebDstttuixsiQrpFJtZCYexxzCIlaBs1NaItFzaosmFf2gMupUTtO6TL9C5c4hEH3/72t/W2zbs1wZF9ox/ZcrdfZJnwDI/Y1klhDrAoyGzCD4f6BVY2MwbuvF25l2Wfn/VRNP3YKsacOWqypJNEKdOBwyNF3E7uqTM4k9Il+lD1U4VRbaRWtBNaW26C1Evg6sxZJnURfXhFhoiU9Pc2AddzTW6nF1S1PGE1mH3KUqnddt52r9122mf3e6TD9jINpPyvI+AILIBAna4WcNX0Gmkg8hysRvNEAzR/GwStvuY1r3nc4x4HiUK9sDUeMRMq05VRHdMi8whZwpG0y4itNLhcqKN32203FMvIxESEYlN0K79onnGAM+3RwZ0JXJrbRbOIe2LBC6nFTEsKSR900EFNWWnLaDvwxcvE38ZD3dD0lkYKx9hHN7htIp6Gz/C3lf00RyvgQcOx5BjuA204yXT/BaNloGEeVpMIhZ0t16LQcWkalEb+KKBobtjV//IKPKk2PKv0cWl0AA9WbRmSKJxYcdksVc8IJ/FjiJd04tHqefBv3I8pvCIzVD9i72akiVO5jB6aPwRKmOmItcnq2MbUcO6hB+44PPAvh95nu8FMmVkOy5gmj8oRWNkINPFHexkRt1mzSGsa6I3mCfJr5Vt0i8sDDjhg7733ZiHvve51L1THu+66K9Nw4Fc8RsEFIid8mBhaRZaigXv4wx9+5ZVXMtwrZ2edddaOO+7IrodIzCeddNKXv/xlYodiWUZ83nnnHXLIIVEWJxz6CuJgWBkzzfett97aKp1z26vdbXqHhSRY3cN7ibzhjkvGgcOvPvBLAz7jB+Tx14p2mmLc2o2mpA1TYbPMhrW9QktMy4nXTACNaCmsKAFHh/MbqA84QAKew5mxogmj1MlYkeZwltSqUcPxR6a03qZeXOEVORMBdzPSpIlcZvch/ypS+j98CplMlROkmc7+gH22G7XJhdLqLHOyPDpHYKUikJiAITOY76c//anaJA2DSe6cEwNkF9gXKUFSMu4lVUCQtKrabBKaROpF0v3zn/9M6/yLX/zi0ksvveOOO5B0WeZ7v/vd79WvfjVvv/e9733+859/2tOehnYa0j3hhBMYIWZt0g033IBu+Rvf+AYrkYiOZlEpUf9AZlp8outEWKGdQdgJF1OO2AvLGp6wl3MlxR5MYdclGiZs+aVZApyCJfSjVzKZrhbDr5yu6lfJMPGXX32kuKXoV49zq/8T+I+sI/hQxLBgieJskWoRWPMYsNYLqz/XwpNZq+DokzW7jGYrkYYyZp5AEr1S4FQbfFHVA+fWA9ArIlXlD5PJEoW94h0DSrHI0E+VgmGOA+XNh1ELa+vCtMQVn0HPgCOwPAgkJuAvfvGLtEfHHnvsHnvswfwmhldppGgZWyUXOuQDVUOGAeLEjGX0pbHAl7/85eiH3/GOdyBMM1Xq4IMPvvbaa2npINTvfOc7WB5xxBFPf/rTn/CEJ5x55pmorImO8WP6AbyFoU899dRzzjmHrTxICRKJohDTk9oooMP6rdI5n/1cnqJdnZ15Dj/aZdkEg22ExSMrlIJoYHIUBiyDDW/rvueLfat+h8qdUVJTB7MEZSBTQfgv1rL0gKhYSP6GErmtX5wsnOfVRLnKtJ2RcLxvWEXcrKY2j/WrMb6OzWC2mq2VORCpyNGzRvZM5rIfLtn6k4nXnAjJcQ4cQtjw3OZfxWbeVBI8h2A5cNBs8kylq9pm3py4xGWu2fmplg0laJOA8ac9T8P7kN+6Q4VsD71whbpty+9skzPLZqEwZIoPtl2pZQYydhwn3ZGwPXRvZbwXCs/zsPUikHjA5onhuu666y644ILzzz//9NNPf+hDH4qEetRRRyEHw6zwH3OmIFrkV5ivWTgWIwKGtHZRHkU2/XS4ZuMEZaKshmWbX8kj4cPTV1xxxexXigK+J0bIOCoJExOwmlVFEIS10LpMnZ03FXXDJTwUMWUl0pQD28CDa8rGRhdFUcGwEm/GW+nsprHNa0byuVpmu6H0ho2pWzdVdl2bHSZr9DcaZyMaT9sOYak7U6m7JzK5idvvOcKhgXRHMqXJiSEOcDCiNtciwogGDI3ltoVKvjqxoZxD7CqgSiCkGlQAEebG2fEknbrlzk2sAN6GLcaiz7YNRCGRPfQYynYuE0cN54foK6xjoXeteNdYSfPqqrUqu00w55tEjhdTHHk8tnH9iB2gDBeFkiUTMjR6D22nYmt1GAG1+Q3UblsAHcqIhd7DKTthun7ydHaIcXp6JFM1fGvNkqfLEdhaEEj8tUjzzCgs+l60vuyGcfXVVyMQM7L72te+FqpjWwzmTMGO0HAz+24tOU6aDhog/YLH+NRsqAeJVRD7IBP9mgZ/jbO1MrjZY/BR971C/xh12b7ASMBV5FRGAScqSLq6mCUb0AtPaO9xzKvNk6VCtbiGHVlCU97olhkY/FeNbKZhbAazSNmVzVXb9iFccKQ54T+CsAXLVK1qjfPwOoPUwqmHzJIoW95NmKS9kCplUW/Xmui0Rk/COhP2q9n6KzZgZHlV3XfTn5CSOeybnKwoo+WHorDV7aHvCB9TzaFjq9tmw4twcOeKypUn1hHYkgjwCSW7ECjxwGDt5z73OSRglhg9+tGPZgtJlu2+4hWv+Na3vsWekdJIsx+kBOJkEbjrlYZAqBLGNOpvBYU/gtHcFy01IwhhcMHcQGOc5cBqFpvW3PoiZLxQnXBiagyU9zhnYFZjyGEMmFfzz8ZvHfzcb4hBtV2dzuhIehQNdJuKJb5wgyPgCDgCSRBITMCf/exnLwwXs5Ff9KIXnXjiiUyJYvjnwAMPZOwWOZjJxsyywZJkRN1vkiS52xWGQBBhbUdGDeeHucotCRiiZqb6aDYTlqHZ2G0hY/PV2Sh7nmzbLOhampCN523LYVhPDFz3xIaU7Nfd3fpGHJLOSZ4kWaNeSD90FSBg+gQMtcyX7nmy5K8cAUeg7xFITMBMd3rsYx/LVCyGfq1ZNDmkvhT4lltu4S2W0Z6Gq6Fg7HukexcACYHIoiLgsBLMul9zXpNhyVB+sDBo44lWeQKVzunWLJF24bsmam9y2ZiaTBBahjTIwUldugiTi9pLCplMUCdgY/36VS7bbmtsBoUVY8OmmfXLEXAEHIEkCCQmYFTNKPqkhVNEmDXdiYFhhoRpCnHAlhrQMCPBSRLjblckAjYoEfbEgCahqyABt8wIE9ipMEyjk+AoAm6uTjN8Bgep0bBtFsoV8aJprok0bA1h/BcImDR0UQK2+escrxjkcoi2Hi+JY853xjYXQetD2nJMxCIBJsHPSLg/OgKOgCOwAAKJCZhtjOBXyJVGBzN3xnppeeP8ZxpTJmFxRu8CMfvrXkHAZD8OBcrUx4DnWZOGw40bbaR2xuy8sLZlPjhYRo7yVyFDt4HsOFq57iXQYRFJFXl0vlCSv4tjwI2opmKk5vMgB+olJA/efTgCjkBfI5C4wWJRkNpBmlHaHckuSL0sC2aDDlYlASfTcJCJWcIrabivAe6TzAdtMAOiZJdCn0cdOzFhChJc1pW68869gti1wZg2u7CVprpsvZKJwFPo2mFIbFY6T8xTbtsxSTWu6m1BE9X0sCFgbEXA7QTobhwBR8ARmIFAYgLmyF6WG51yyinsK4ki8Xe/+9273/3us88+m10eOdMX0uXwA9omRGTGAnmcEZ8/9h4Ck8VKLp9jThL1gaI33WzrTLJKDUVxHJvIZ1k9xOLa1h4CQzOUQZdufGKSkNkXg8XdyNzZ3ACTsqBoKvHtt98+MjykoeLWYSV4w34fG2up1asH0EIzbbsQtl4MSeVoL6g+jT4c9lUNV88jQeju1BFwBByBpk0j2gXjE5/4xHvf+96nPOUp8CvywfHHH7/XXnuxC9V3v/tddqR661vfevLJJ7OZM8HRNjkHtwvrSnYnMZEciIem5NS5MqWhU4TguV7ObQc72/ZXGnY1rS/GcOhFcC7uljyaXVCXHby0eYP6JZ8H+XeaJyKlQ4CVzzGchos/OAKOQBIEQsOWxAMbNT/4wQ9GzQi/InAgHLAAiV2a4Vr2bb744ovVJPFIsyX5IEnw7nblIRBnQWuHMkYo5hFoeQt1UXPmcTMNAtsSg/VsZmd++SOfHHhlWmi7+FOrlBCK84XEGh2FMOedeCB7qrGxe4hWXQ3d+QTwpZ5EYzXynMG4pSPgCDgCcyOQmIDve9/7vuc971H3nyDRJXJAwv3vf3+4ljOLmAjN+QrY84iOTjsnzB2z2/YKAkEkNVqEVqGr+WdB0zODzzg4sv3cE/KgnXWYpjqVNApsOm7xsAVjeumun4YUeF2Lk0mwRqxjmolbMyHoc1ha/HIEHAFHIDkCiSUG2Pe44477yU9+st9++yEK/PGPf2Trqy984QvQLdtjsSsWxxkxxxXJgOZ4VsOVPIHuY+tGAPqxThyzoLP1U3uh2HmSDD3Te1MvDWd4NwXvFJnO8hrOE2YONFRX3rzR9sJSneW4SfwGjxiMgCvlLq4DJkzyJQImwWJZSbqmmm4i4BncPCsDbuEIOAKOwNwIJCbgww8/nOP/PvKRj+iMXh6/9KUv3fve9yb45z3veTSmUtCtX7+eRtbniM6Neg/Z1qcHIyJmkYCNi+eXgEXAkoDbkh2DfE01HRoa2bh5IwdPl4dSYRvpoLzhzKIwPaohAXcNWeVLKmgIuK6DJviwDpi/UQJ2Au4a6B6QI9BnCCQmYM5dgH3f9ra30ZIilGjElynQmFn+C/tqQbBaWJn7DNL+yq6ISqqOwUHLezz8cU4geIvjBGPAhAJRp22R8SarXbUyhx8hETddpCHM/EonmdrV5H8uYyRgXpJgPWI2eT0I7HEMeIqb5wrH7RwBR8ARaIVAECNavZzLHj2zWljaUNgXhTOu2Bca9kULTTvIIhOt0MC+izsTzZUWt9vyCHAiINticJYCvMTuVtlqabIS5k1Z0mYKh7jZXM1OpvMDhTxdPzxa/bMNrUJHkNcm0NZ91edYpTOlao3Xo8yE5hjDSqrUcMBBxMSOZyZ9FWtsT1WBl6cxs6VhgcviDCkJm3sEo069DfkK4jfHH6Y19EzgdlKSnaKYKldQupc5CteORkrVlzVbWGTIhHLLWUjMTBCCE785Ao6AIxCaiUQwPPnJT0bnjFJOq02QdKPKkbW/WoiCIVGY7niFIgDrskAWOsoPjDIneG0+NZwtTVTT1injKk3UKcgOtzcyws1f7y5WR9auWztcmawhMMOsbKNsc5vtxF3csZzYGAtS4w80VuLshWya+rTjUKFSzdx05935PMfx1mqTpXQ1PVjjuMAUhwTfWa4VJyZ3XpusQodYYMoqVJqtVa0TUT/1Nj+QxTI1kE/lCtkNE+WisSypK5EYcrGZc4zzo7nyeL48NmRn0ZNUtPD2hx8O+NkVfMnod0fAEXAEZiAQJI8ZdvM+omr+4Ac/+M1vfpOpWGjhWIbEVpTwMYuD5/XnL3sUgTCDCgblx1qhXLo2kco0Tu2t8w9K2gxSYRis3VSulTKFQi5bSJeD0MmOkplwkgFEGGjLdrky9uUt90oQjlk1zJHAnLjEOiQiwj5nS3QljqaIrsxpShk7nzYIncmhNsUyoVkyjTUtsRCwpQQ7ttnkrKXGZblh948y3QGTuTGaF3YE4eTgINLXTwtuuPe/joAj4AjMjUBiAubE34c//OFonlnyy9Avc6F/+ctf7rnnnnMH77b9hABUhAoE8iqWUjU4M2xjYfrYKfayEWK4jcHabDhGKJCpUZhdIj+ZA5VBtPLNG3p+vGHqUz0wFMAmrfLGeJuhWYvanrpz1YL0SsrCREKWAk8Fq0g0BqxBFuJtyuKUSzc5Ao6AIzAPAokJ+Morr2SIFyUzDVDczwg5eJ44/FUvIwC7BgEWBoKujJCKqXGUwhBw0xF9DO3CW0iIpQnbwXFwgO0kg4jLdsqBhA2iJqMQazCzPWlcg7oXXqUt8AbpsSUGu2VQG7UiWX67cicBYZphOWx7Bd3XGZ6/WuMeCbgr0XkgjoAj0FcIJCZg0KEpvPTSS2mYtttuO8aAUUHHrX37CjvPbECA4d2gSw4PyKnVUmpsIlVbXYcH0ZGf6tlkyXZw5Az7oUKwMdF4ltAalNUNbPFadzAyNAz/NSYcYBnYOTAii4ORgAcYrZ0VWCOcDv9aNCjI7cBNCyEQsGmpiVtTEXWsU4OXO4zFvTkCjkB/ItAsY7SFwF/+8pdHPvKRBx10EBtPMg8L9mXzZ1YAt+XZHfUeApBokESlZ5ZShOnwltGGhGqraRF/a6lNm2Gv6qANAAderUFradNRT4el+TEbtMxUU0nAmzZz/oIxep2XA/eLgPOFbOLaPD3e2U8EqIV2ELBFanmy1MHBkoCtw2HEHN75zRFwBByBJAgkbrJOO+00lkVypg13BuRohp70pCddccUVSSJ1tz2LgAiYWmE0FdbMGj+xTTOMVamySRbTrwbyGQRis2M+U5r1QzgMgEBjczCZrbPlaowBa4LX1GEMvLXjl6q1PLOwgssu3ghQm8kwBmzDwGxAXU9rOHUxzcpjDuXsYoQelCPgCPQRAolV0BdeeOF5553Hqt/tt98e4YAxME4IZjfKPsLMs9oaASlsyyXIiivDqqJqzljVaLJahZjREuc4tMjeoruWsyBcmiOouM6sONCCYKzlXSvcShU767BOefgOAaHWJiiiRhWO4+5eGveFfUW0RsAazw5aaT4Bs+96rN3Ng4fmCDgCWyUCiSVgZsHAvuRFs0Bp+zZs2IAieqvMnSdq6RHIZErjtvmz1LA77LBDtVKjSljE+RwaYnHTRJEDFXK333EH68e3X7cWy0qlZFstBxqbLro2P7Hv1aRolVqH0oXqN2lBh3qbS9VKJYK6++67ocl1a1bV+dwcLPaSjpwZX2w4A/dv2lTJZ1kHXGHumHUmQvBG/Axv29nEi43O/TsCjkAfIpCYgB/wgAd87Wtfox1E4ICDkQDe//73Mx7ch9h5liXCSkbURCyNmJbC3smiybrAGuTZcpm1s9VCzo73NYuw50YTjMG66RkpOU5sDrJmTbK1hRmIPR3GgCFC9tAw4Xua3y48EKByFzXPMVCxr/LrY8ARFjc4Ao5A+wgkVkF/+MMfPvjgg3/2s5/BvuwIzaqka6+99qabbmo/SnfZYwjAfFELy0gt1KgJSmRTNGl8GXp6zGGGd0eGBhrEVp/AZYDgaOZlciXqavPOllvhTOCpdcBynzEtdqXEZpFpxoBnBrDoZ9JJ7ggG4Zu7yNgS39AAaczbCXjRSHsAjkA/IpC4zdprr71uvvnmQw899KijjmI7jqOPPppWVXJAP+LX53kOLMjYK9UITuKatlqXxb+BWOEuRn1xOzaGTre2enQEe7PAagZ3KZRGaObMbCygwaE8NK51wAQYwsadCd6B76sDhYKkcGwWf8XZziJg5F1LbEMeJz1+GtLiQfYQHIE+RyCxBAxerPp94xvfaDNdazXkEhZExuNd+xzNfsw+wmk4NVfUCQFTK+qrdWv1CcxQl7TDOrpjzepRc4zW2OYvmWTJH2Rl24rZ7MN91m3VsIWyeWI8rMgNXnEfHE9O2u4cEpFn+evQQpOt6FjQuUTwhYAtM7ZiylJJtCLgwcGBMDu6w1jcmyPgCPQzAp0QMCcBswnlHXfcceedd2oLDmbBPP/5z+9nHPs379VyKhuW4oTlvEECzoyNTxggxq9GsFAXBIxhw6ZNzFhCAjbFC7JvuYKK2Vzqwlj3ERUz5lsPw8OmDZ5gNpdIO9z1itEQ9NSFwvRDChuhLuYvKZJ2xwiYtLD9JZkKE7REwOjFZb+YWNyvI+AI9CcCiQn4G9/4xtOf/nT2nmR2KPpAhAM2A9ppp52cgPuzAolljWoDkzZW69pUZY2bYohvpUAeGRo0t3gJDN0aN2NjXZA1u1dCwMXiOLZTQnCIVBPytU6p4WOxf6MEHMeAZySWSKn8TsCLBdr9OwJ9jEAUNdrF4JRTTnnpS1+K+IvUy05YyAHoFX//+9+369/d9RICdSI1KmX8ld7cIKTEeXzlcEJR2kRSjkjKV3VmcIoNspgFzU5YxqBp3rDq1xbv4jEwqaCJdRI7k2xZ5MvSH/aWzqdZlWS0Li40L0GwLtbyFbbECEPR0XM7MFsEQQcehqJtCbLClF/MRMpaJwT4Si2k2TJq3QKWIZVsyXLV5nNzsTU1/8hlWOFMjhIlIwThN0fAEeg7BOZrKLTQAkgY5dW8ViQYJjy/8pWvHBkZaSzAsCZLrVLfgecZhoQKA6wNYhMqWJZTe7cfHszUcpsrhTEk4EKB6pUrTwxlxtE2IxT/9bbNuUr5n3YZvWsyNZHK1wocZJnKcqgfLKglSXaioM10CvUS9h0opfLlik21ggtXDReGCkO33W50ieo6k6mWyiW4/K5NmUp6dHiIlbqBQROUC3O5bDoXhx7W4M50GU5F00yY2YyFRmrWFAaL1czGIltYk0bODLZZ2eRlE0uqqpXVbGpNysLZhRlzbinIkfVU2DDE+DhkJUGS3Kkj4Aj0CwLztQ4iXe4omZnhcvvttzPC95znPOf888/X6ot//OMfCDywshSP/YKZ5zMiYL0vztClFtkC34FaahBWrNQmSowMmxBsAmqVkxnGs6kiNpOM51bKTFZmL0rOELTjf41bYV+MEJ9dU3pnNtIKj2EtUwlyYxtLokO8xo3FnDZBGjeQYyU1gDDK68SXxWfzqywsC9hmO4ek2LwxMsawL7OvkYCx5wdLcydrpRSyu+2jyXTuIIfX/dufhpBeNQneL0fAEXAE5kYg9NnnfpUSy0q6RRrW44EHHviud73rsssuO/bYYxl1w54pr5yJdMwxx7QIxq17HIFaYymRjdQODlJhNAvaOJIr6GzRl0BajFmwmfJgITXQoEo6cOYGxjJV9NyXqXkDj4V+nqlj4GKb2AUdhnVBFqydMWyiaHcv4+BwGhKKb5Kg1GKwngEHKbIPiB0WUc9od6P20BwBR6DnEZiPgOFXWlImW+nkNdYaIQ2jf+aO7PuhD31I3EyzyISUqK/uecg8gzMQMAaqwcIm7WkZ0vjY5ik3MGuajTJym8aRLtPswTGQN1EV+oS46uylDTum/NRNuOEyfoOhs7YrZCq1njrJwHD9zdQ64Hp/Mdh37UYCNNQCAUO6dQKupSoQfrXK+cOmCe9abB6QI+AI9BcCLQkYTkWkCM2ONW00Q5yAtG7dOuZeabqpBA4ccMSNnwfcX7Vmem6REWuVUtr0wxAwfFnT4AWPtv4IIdX2wcpsZFiYWVqsnNVcpyAbR+WtvZvrMvkZBg4EzMwD+nybNrPqd63camMMRTdQ6KYEbJ3LwLcSsulfBhHYoiUtSNw4SGeM/13+VVn43RFwBJIi0LL7LpZF5mCeM+xLc8NCI8yIOFDv2Weffd1112Gp5sn3gk6Ke0+5T4eZSyFLg4Oms61ByDGHJgHbaO6mzTbleWRkyNg48Bbio/EcV12LOzViGljNhmL1XvoVaiOiMhIwL8JOWOaX/xAwXMk0BXy1rNAWTbIrpKxpIw5Ljl0kibljdD3JKR1YItXuHHrrd0fAEXAE2kSgZXslbRtNjNo+htmYe7V+/XoaO4RjdoGmNcQNe3Hg4PLLL28zPnfWYwiYXBgvZi1nUrYcKJ2eZKA2XsyySqU2jk9An6tGRiQyqubVCViSbHDfoDl7wCVTnsLd5kjZGHAtMzlh5M6kaURQDLinQmJQlxFDV656wmD0kDboVl0BAhcB40DfSLBpRqEr8XsgjoAj0PsItCRgNWq0OxAt7CuFM20c7Y400lAvbrbZZhtYubu7APY+6j2UQyOqhh45GI2xuODEirg08BXGySIjwdmBAVveA21DnqHy8cYYVitoWwEjOhTFFit2GKBGjRWDCaM1luQaH3f9EsuSAPKhC0P4OQF3HWwP0BHoLwRaErBgoCXFEGULBuFkz05YzHyGiRkA3n777eOYX3+B57mFRLPsk1zLFwqlYlFy7PDwIEqRsTHTK4eZS9lUvgB5jY0Xi+XqqlGTgFEW23If855lbnRrIKvQdaVUyQ4NsfqIKQhwLXUP91BgOpNjL0uCKRN3sUhfcIHa3Dqa2W+o+aQTuZuzruFepj7YYRCBhNE/F4v2Ufjqu9m4uY0j4Ai0j0DLJiuSLu0dQ780Pdttt91tt91Go4PNrrvuykRoLKFkRGE/jKF9xHvQZZBGoz421JwqEjDDtMaPJpfait2JYgm1cSGXp85hp7vxpzmwC6MkWj3ac3CWQQ0dtukg5Go6w2HDOIsuzRf7Y6RqrFYKPuq+u/WHfHGJehUmHQs4mEvd025F5OE4Ao5AvyHQchY0IoXNakmn43ELEO1JJ50EDWN/4403nnHGGUyHQQhG3EEL3W/AeX4jAppBBRuZVGqbNg8w45mla6z4DW7Y7bHCPOiNdhZhhsOARb25Bu/GGVgxwGCIJEvA1k0k5IEhliGlNk8UIVpI3+g23G0dMOucclNBhhAWeyN86yiE2OF4eyQRTOzOMOpsgWtcBoPZ++UIOAKOQEIEWhIwki6CL1xL08MsaBRxJ598MoO+GzZsYFT4cY973M4778zeWDjg8alPfWrCeN157yBgAjC0lM1qoQ7bppG3zZs3ZdIcuhCYKdzWb9jIwp1VI8NGp4Hb6nJsYDhsolAb3oebcawRHu/gOGoaFE+3D0tRnhFirT4LOgwuB1/duxFzJOCYPNIzOWlPfCMhgU7A3UPcQ3IE+gmBlgQMCKjd6NpDusi4EO2ZZ57JuK/AgZubB8B8DLif6szMvBoLBoIMetr0yNAwOuPJ8XGW5YZ5UbBwFr7asGkMMqMKQcemRIbcIE9JzeEMg5nhwuoWctW4tlpjsHhkxOYAioBhPsLgVdVGZFmGVGOK9FJc2oiDT0DdC6IIBGzHPYmATTJunGW8FAnwMB0BR6BXEZA0Mnfu0OzxQhtBQ8NiXwxYRrGAo5Bwpse5Q3Hb3kWgIf/BhWbUQCkVBrNOHqxnvWb7J9+9YROzoEdXmRq5FsZQ62/tWSGFUKZssa+HmapZVbSNsEy2DhJwwxleqZOsNeZkwDT9gIb9Iv8qLwRC3aYbagRsQq9dswlY9n53BBwBRyARAvMRcBziIkSZaVVl0LQsCTSICJISEkXsjnsDAVGn6Ep3U42ka9q+1PJYsyW7/B0bG2ct7cigbcQBIVvNwzrMrpKR++yrToRBDpaMOz4xWadrEz2NuzUGzBkPXbyIV1mLfU09EgWRqhvKtxBT0sWoPShHwBHoEwRaEjDND2pnUKDvH7FgGyweecXMLGmnMbNCwyehRIj6yiBxkztHIJHxWsoOzc0yrJEuT5ZtmNSqVzVdqWaw31yEOCuFfDhCN0i0yLeBzhpirh6mECRgxYCVKbEHs5waOAndShYlcAwsZ0I7zDRoiwsROOnVODEwxGQxwuP8NMmLMFlmxdA1+uc6Ads0rFSpwrxoNt9Mz9j9sp5cwjS9OUkKG3YlTZK7dwQcgf5AwFqtOS84VbQ6Q7rlUfb4QgLAzOrMOUNwyz5AoJpJMxo6zpaM5QmWA9sJg9tvyyjv5rHSOFrjQIiZcpnjAlOTlVItXdlpuzXQ1/CAnXNfheWga6M34yvqon6BxuAw+9nSI/Zezo1Ce2syqVVpBOBxm04N+xJgKnX7hlRmcDDDscRGlkkJmM0v7QfjEmDNDvRl2BqFdomobXIZu06PDMHxd41NDubrndHNldQEpwKzp0g1xbp4eql0SsmgxW1K8DKEzQHD+A47lCRNUh/UGs+iI+AIBARodvxyBDpGAHaBc9kZuaKDcuGhHL2yVKVYNna0KVI1NoI2YmQuX7Va5sSELE6nIjTOVZcuMNbMVziEICtsN02Hj8MeoMV02s4W5gp0W7bBWTZljtMJExCepTAQp9KDT1ONI53TNzBzOLUJCZgEBKdKJ0YkYPKV56QJY9n6tlzBCQAYGQdzCN1vjoAj4Ai0QMAJuAUwbt0uAoG8Gsdy8DBYsOnIzEyGz6QstllLYaQWbmJYY4ZOBccitrkjhP1EjxA1G01z6G86M2FzkOu+whRoO4lhKaoyMccx4BgjyYhjwErzfOmXC787Ao6AIzALgaVotWZF4ha9i0B9HJcBUiYiB3FQs6A3T06YQIl9ml0bmYGFpGk7indwZmAYTjUEWYlE4LAd86CjiMl8QGyaF8V1EexIwDb1oUH5dAiIlFg0SQKDE3AXMfegHIH+QcAJuH/KeolyGqpQIFoR8Ogwy5CqE+NFYy+bkWWDuWH/5tTI8JDN6wsXpNUeb6HQNWonGnTXEDDq5g2bypEOxYVDQ4OBkiMv12NZzB+FpUkPbEcTVdukRJGSGNzwq+83spjI3K8j4Aj0HwJOwP1X5t3Msc1gMnIMU4R5gCmHBgusMto8wUypVI3ZTVhxFuHGjWzpvGaUSUtTl7S7JiW3dxFTYXCAQWAtBYaR8cl6J4gcCTgSZHuBLeAqpkkKc1sHHKxILRmKEjB2bSd/gRj9tSPgCPQbAk7A/Vbi3cwv9MMEqEDAtjoHZoJwB21BbnUi7Ngs9mUtzuZNY+ladSRspQGHccV0zHiM9mawCEIkwWgEHPYnH58smlxsUdeHYwfyhaCV7hoLx/RJTLe9oIOVUh7HgKMzS45fjoAj4AgkQcAJOAla7nYWAjbQG4iQNzbdmbnKWXvWJKzIh8ipWA7mTSMdyGyKueYjYJvnbNt4IFvLAzptFvloj7bwzpYGwZHZHAtzZyWuGxYi4JDIqeCIlAeEY+IUJU+9c5Mj4Ag4Au0h4ATcHk7uam4E4NxAu7YKyDZt5s+6tbDvxNimcdYniSMRYjk3Op/NjgxzPIPxlpTPCpJHrrmDxzaTKU1M8F78ysGXSJ+bNk8QDuRH+DwWSxNIxjYNWmrilmEleEEvgShKldS2225LZ4I94AZCGkm5rTIK26RzJ7/ZTJqNQBIE7U4dAUfAEQgIOAF7RegcAWgnSKeqRUZCxq/82FsjVYsEDFNCk+laZYBFRMkvBo8JVlcuh6q5LgFL4pUwms8wQ4voSUOXuVBzsJvFXBITJWClSlJyPYn+xxFwBByB9hBwAm4PJ3fVAgGJofWXthYplcum8gW2vqoxTstbtqzgYtoUY8BMkI5U2iK8WdaNFca8gFrzTLZKpxgDjjQ7Wa6EkxhMuR13lp4VSocWsC+rfuHXpknQpmD3MeAOAXVvjoAj0ISAE3ATGG5MjgAq6PoYcFinQ31iD0b0wYiM4xNGmcbQaZYhbapVy6tXTZsF3WZsaQm3gbrZjRw5FzonWCy415chsT64zeDacyahVgSMDwhYlC9RWJE2H0fYXqjuyhFwBByBKQScgKewcFNSBOA/flOXbe5sw8BGwNXUeLH+FkuWIUFdq1ePTjlu04RWmW2Zw+IffIyMjBDj2OZx7jAiIWsZknb/QFZtM9QFnTUTsCTglgQ8DYIFA3YHjoAj4AjUEXAC9qqwWATqBMQWyDV2hLYdmwfzhUqtOjFpUqPeIgGjgl4zuipxZGFeVUP+TA0NmQzdWAdsgcU9MUSQ03sEiWOb4QE+jypoicCzJeAZXvzREXAEHIE2EXACbhModzY3AnXam/6SI4ywKNbPTLB3zCLmPjzCJlkJL2Y/M8uYEw2DP0m6kC68LmrXXtD5vK0IsnOGu3RJlBYBE6SpoENWRcBTY8B2TGHXIu1S2j0YR8ARWBkIOAGvjHLaOlMJPyHvWh1C9xtOD4SksGQWNGcTliqBstIpzi4aZ+/Iam2AF0lzYkzIyh/7wzaWw+GQwMmKhclyIO7lSiVXKw1yohI8yM5b5rDdS25DkiypDSINGQr5whRyZm9CjETPmHeqxL4cDHVn7aSncNmBESG0MAe8brYVzH45Ao6AI9AKgcTtYauAktrb8s1iEV+M4WlfBcxYIk/Insdm2QJn0Z5XUScpDWR8lGiie/NiU7z41XUEYBvmROURATkQmG2fc8ZH/HZYt0M+v2bDxkkoEzcTqdTfNmwujk/stn0ifgzpzebQZqcLdrxuvpjaY5vBdDV998Yia5wI9m5U0BXSMLk6WzYXNRYphenQ7WaV+WH84FZLtn4hyeTHjhUk8aPsK5JJFytlYuQEhnImh6GYyo+NT67KVwdSFa0Dzqaq/CqpTDl0SPDo7NtuIbg7R6BfEdhiBMzBOFxwKkpFaSzRUrIhAxNeWGQJ3VIiEDCTdzDA0Dhjag9Ey2gi/Aq5Qr2MBTITFcc8iq0JE/e640uWkZ41s6Zfy3oJ8o16GHZEOWsCIKf2mlRqk7Ay2Uoly36RVC+EQLTP5Ux2cMC2suigwlWC0EnqB1PVIeZkVWsTJajO4oIyy9VKtloelPI5nUwCDogQxtRlImstpNGWVCHl2ls76rhW43xjYsQKNxg4JTGfqWU5PFiX7cVh49W8Cv6nBVt3438cAUfAEWhCoIP2sMn3Iow2KyedjrIvIbHCBB7dsGEDBuiWVzyGZSfGxHLJ29HRUfgVGwh4eHj47rvv1j5KOBBtxzBF54RMOEqpE/AiSqyVV+gpwJu2P/qpq0RxQEj8NpdsKS0TmBMJpzE+9aJ4TGcyFD1BaQwYG6KT1kRdruilK4ZYW1TB2H3SuhkhaPqIvI0VrCvReSCOgCPQbwiEpnNLZFotJgyKEExLDbNKw7x69WpEYWgVsRhLCcc4lhQb21kecXnnnXeuXbtWljSItPtkJbabkXfVguNFGxhtiez2aJyio+mzkBBzUUtQCpsnbLsMnHAWIeCPjAxBxh1cBFX3mGGBk21jSVWR4ElcRsa1GjF2EPL8Xui2qeaoEpZDlGSHTFGRSAb2sbLNH5S/dQQcAUdgNgJbjIBpMVEv05LCmsi7//mf//moRz0K6Zatd2naoFXaOCiWFNPGcW233Xbct99++/POOw9L9hbmftVVV+27777YH3DAAeeeey4eIW/uvKLpRDai3cdAUDKoPeWtX11DwFSyAG6YGx0HzSsFinTIqD0WcOf6DVYQo8O2hLeDi/JVONzpYhEyoU1a2Baj1B5E2FngFsqcVwiOCsNf9fCoTUTHDyUzWhZSBf+qss0ZgFs6Ao6AIzA/AluMgKWClsx6++23Q5bHH3/8c5/7XFo0WlgGd7mTdOy5f+UrX7nssssQi//0pz/B09jAzZdeeukjH/nIhz3sYTfffPNLXvKSF77whRdddBEqa3hdjTLOaCVDQ5lTsIoOe7+6hQD81GA+m/SLrEqVGizka5nseJCAYeRNm8agzNWjw7w1Lk14McEaXxKzWd8EHVKaYZKABYQEzH2Q8eFGOhIGP7fzZqm+IQHXh3vLzMEO4yMkhrThnzo2dyhu6wg4Ao5AawQ6G5VrHV7bbxB2acWQgLmQbt/85jfj9WMf+xhkTHvHBfXS1HJhz/DhPe5xD+QqDsPh8R//+Adevv71r++1116Iztg84xnP+NWvfnXWWWftv//+yNDYcEHhahmj4EuMeuX3riAA5cGv3EM/DgJmrVGWiVB0dGDczZMTmpE0NmGLgFFBZ5PzFOtsRXJiRHaltDpRrrGuuGL6EXXRMkjG5iB5+K1woM4Qr2qO+oJUHlYe4Z45Z+SOyc/xDKdYwVqF5vaOgCPgCMxGYItJwCSFZouWGibGwHAvNjzuuOOOUDKNHc2sLCHjZz7zmbvttttDHvKQc845B2c77LADMtCPfvSjww47jEcco9A+9NBDf/jDH8K+0k5jTyuJ8hkiRy+NJaGtX78ee7+6iIA4uB4gO2HVYCnOY8iydmiyWKK/A0OXUd6m04MDnBSc+JrGbUQW+lUwYqm+ythKOVjWReTEEbTw0BwvlQ1X0UYETI5s8W+4XAKuA+F/HAFHIAkCyyQBB0G3CNeSNugQRTE20C26YuRatIga7oV0USBjj7YZN1jyCrl2n3322XnnnX/84x+/4AUvoB3kDpUSwjbbbAPFwtAEi3AME+N91apVPCp8wjn77LORkjHQjKqxToKPu10AAdjIDustldMMwqZrhXxuYym1/fbblUsVziyCuCDNf9x+B5tTFrJZtBkNzlog2Pgabgu0y7G7qSprfrNZJu7dPjbBEEY+teYuRO7QzRpgbJi4mKjcAcnHyJoM2VymVK7mkHMD9VLHqGkI9ATPqAhVNJ68VK5UmRnW5NWNjoAj4Ai0hcByNBw33njj6aeffu9735vGlIsGFNLljhm59pJLLuGRxNKkMvRLS8edSVgItbAv9xe/+MXHHHMMumVGiN/4xjdCqDiGbm+99VYIGGEIFod6NW8L9iUcaFi5x/J1r3vdn//8Z/UAbrnllrZQcUdtI8DEJBGkybphLSyURaFwlat2dj0/Cohu00ChkJR9Z6SCCoMNNYTQNPTLkAJm4qIe80u+z9aMGKY9KjqsZIi9tyByWx6nufYHR8ARcAQSIrAcEvA973nPV77ylSeeeCJyA4SKBIwsAQGjHIZ6d999d8RZhF3kY4RUVNDQJ86sCQ+zsXCJF9pZRoIPPvjgt73tbcoj5Mr0K8zib+Zn4YBWkgZaNnKGdy412bFJTYiSO58bARsADntVGBcZD8PBFaYGo2xO5/JSQfNq41goviHrZnV8ETxzjvGupeFUCcwIo5QsipOl6EhSWwgcmZ0aRVx08pR4pkNjwFL9Ca9UgsXvjoAjkBSB5SBg0kQTyYQpaLU5fbShCKbY0KTSuuEG4rzmmmvgYCwRZKFktX1qc3Hzs5/9bL/99sOA/QknnPC1r33tjDPO0BAdk6KPPPJIuHzdunW0mzCxCJ6ggu7QeJqrOQFu7goCRo0mmxodGwFnrRwpgk3jNgkL27HxCRwwC7oDmtQMrHo6g391pzTRPVSfoBDuSk6mB2LrgI1qpwiYTJJZ6hW5ozfpBDwdMH9yBByBZAgsOQFLcdfMfPAu9AndQqtcGrVFn3z99ddDotDnlVdeCfs+6EEPIivf/e53r7766qOOOooW/Hvf+94nPvGJ5z3veYSAqvlJT3rSBz7wATTMqKYvuOACZlDjGO+0jzgWcwsMJGkM2N9xxx3J4HHXCyEAQ4l7jRzhq1oFsAfZsLlaZeIbdMWPMQVKZNWqEZx0wMFKgmZBY9asPRGwhFEbkQ1XtVLL1OVSWSz2bhJwg4CtXoXw1GukN9kg4MXG4v4dAUegPxFYcgKWeAq41hzXarSekK6wpl2GmKFSmjZW8X7nO9+BNaHeQw45hOb1wgsvfMQjHoEB0j3ttNOQetE/v+Y1r3nVq14l7wceeOBnPvMZRoU/+tGPskjp4x//OEuEkYAJH3ZH6lXriWMMEACWMTEKwe+LRED82ggELg4Co60DhnczY5ttK0ouCppCWDU6Un/d8NDmX8Tq6BITEjCPDRX0NGEU1s/YNKluXMQkgm0QMDWQcLEWAVOdrOdBn6O7I8/dSLuH4Qg4AisCgSUnYForCSgM0EK0AgVapf2iJZVyGPNXv/pVmlQMkm5FmZgfHy5rWMMGhAoKQZbQIO/HhQtnhMwoMnFhD4tjE2meGLHHEjk4LhFeEWWzUhI5RavIi1U7ygCJlPKaKE5CWTDWptD3GCq4VwAASo9JREFUorg740YRsCRg7pQs1YMiVsliFhfySDerW6A1oiP59flW1FWFLiYmUl6FitqtOD0cR8AR6C8EOtYItgsTlMmE1Ui0mrwKd0o1rUfWCBEcltAkAjF8SbtGM6emNsYkTTKPTIHGMSK1XhEF7IsXDNyx1GBzjJdH+ACSRryOoblh8QhASHAqP07JTaXpzHG3R1vwmxsoQljhnKRNRc7vKwwXbBuLDhjSSE666zAgPJDj1N/KRKnMgVnj1Vw5nS9kapChuQmztJLki/qvn/0h1ZY8k2izlTD+y6NlhVMW07VK1U4CpktRtLOAa3mORcQxW52Gmd644iREuTd7+iGGST3wJElyt46AI9AvCCy5BAyQzXOvolkChNSJzGfGWRRfJOaqMZWlzJGA4WncI+xy55K9XCpYuZfUiwMJ0CYndU9CCjH3+w16YVabScDscVUbSGUxQkulkWy+nB9MDVaL9JFWpdaXBxEVd1yNBZppo6ckVzrPQYeiRWbq5XLbjWTyqYm7xyfYgvK2UmE8NbBmOAe1UyeM75JcgTstQVkOVUyVs2niIYg8/YmKne47OZQ1OXfVYC5VKY4Xa8SI43EOJqxNjhBlmS1HcuMpxoaty2HhGEFXUlU8ZQgEOAguaYaT5MDdOgKOwApGIGGLtYJz6knvPgJQC9tksAMHTGZSap0Ay9SqQTt6ITs5UUZVXErns4Oj1mmqMQYxpbFuO0Ema1pNTVskSMAZ+LBSZQL95lq2lM7mWPRkjBfC0739oEkUju0/7EvybKAXExYc9ssfYoR+STZyOH/4lW2/61qBo4fNEc7DMcD19dC8r+eRfPLglyPgCDgCrRCwVtEvR2CRCMBSTZdx5ejw0B3p2vjmSXiKi8fgZrrDJj9tGW1hrg1VoJS2cQqb2W6nEkllwqvuKji0AopgpXFhTITUh0ih2LAOuJ6l+l5dbWXBHTkCjoAj0EDACbiBhP/tCgINhrW9OGpVRmprY+OYIGALPrH+eSpNxq/wX82GHiBg5vHBhdAwLuK4xpTrbpgyQeomJAiYGDX3ikfNJYT1xffTVip3I14PwxFwBPoEASfgPinoJcumpMKpkU7EX5OA2ZuKeUslO7mvyIbKw0N5U9baFGmbqJXo0ixoeWEbyJERC59Z0ASoSXwQMGa4OWnI8ySDwGJCJQGLd7HXJEEijQSMpV+OgCPgCCRFwAk4KWLufjoC0GO6ISryxsg1zfCnScDVynipWi5OMj94dDAQcNJZUiEqpM/IrEECtsU/koAhYFTQSyEBN3NqVEEr5xK7w5y+oJKWrd8dAUfAEUiIgBNwQsDc+XQEbFqULlFWzWb/YrF6eCBju1FOchIkE4zXDA/WHUb3DX8L/q0TcAgf82DBtmhujAHbcKymvi8YTiIHdbE7cL+m2WspHamI64BNArYJWH45Ao6AI9AJAk7AnaDmfmYgEFi1Pr25GuThkcFCJl3ZOMks6GK2Wh1m0jDiYkfjpdBtZG3MuSAOiwW1mjyuT5uRqm49alWb4iJMMbEsg+K7W/F4OI6AI9BfCJiw4pcjsBgE4mocCyRtq4H4DRUyhVxmbKK4qcgo8Pg2q0dZkwNFdyAwMvgKAdseG8UiW6oQCBuaona+dX1qw4YNGOBCXnZ3CjSTt0ssmkqnNW8MIRvKJ2r6ERigfLoClt8Ma5QrHWTK/PrlCDgC/Y2AE3B/l3/Xcx8WyxLqcCHHGPCmYm1sopTP1kaGbJeozoiKUd56MhuGxqCsbbHCqyWSgGO8MsyWgBvJ6jqIHqAj4Aj0BQJOwH1RzEuayYaCONBrfT+KFLOu0rUyY8DrxyY5BGNdGAPWUfZJE1NX9uKtQcBaCjw5acIokqjWAScNdkH3UV8ex4DFuNJ+x4Hn6GzBAN2BI+AIOALNCDgBN6Ph5mQIQLkmgdrFQYQy1PXMJgGnqpuLlQ2bJmwWNDtBIwF3VN1mEDAs2DiPoYx2GsF0KWZBk9q4DhiuhebFu+QyzoJWhp2A6wXvfxwBRyAhAh21iAnjcOe9jwC8ZIuP+NV3hBxgBJiNOMrluzkzo1bh7CqB0CDsBJBIALUh18ZZC2whDiOyFBguhIB18lVDPE4Q8vxOjVnDQG9cBywJWARcZ32bINb1mOdPl791BByBHkHACbhHCnILZwP2bUjASsnIQAH6mphEAh7LpGurhnns8JKIWWXBT4PpxLgQMBIwTLwUEnBzR6Ex5GzbREcJuK737kyr3iES7s0RcAR6CgEn4J4qzi2dGatOoq7hwQHWyI6Nj2/ctJkx4FUjlrTpHJ0ssXEOFN4aY8CTcQx4MSHPmY6pdcCNSV5afYRjRSpW9mVIc6Lnlo6AI9AOAr4OuB2U3M1CCGhNjh2MxFmBdh5Q2IejMjZZZhlSbTAzNGyrd6HJhD2+ukK7Hn1Y40SVHchUKqn85kq2WMtXa9lCxlYH1SNIKGibRy5LFv95qn8RDGBPjQHb4YQma4ezBlPlajpXLQ/YwRCcncS2I+aZaEPMIZBGGuyFX46AI+AItEDAm4gWwLh12wiw0ZUxkc2wyrHYFwJmvJczesvV0k133jmwdtsN4+MDrKutppiI1eCmtkKH4SqpzGZ2lK5V8/nsRKWGeE0gO60ZnEgN3Dk5cMtdxdzA6pGBHMxfQgueKPSQGI79zaTsZMMy7FsdSEG1QeVdSGXLZSLPcFs1UBlITUxUUptSqQm2oS6nh2rFdbnyJErxAj2ACucV1nsARuF5WwwdtufkIWGK2oLFHTkCjkBvIOAScG+U4xbLBewVJFtTPFcC28CFTIAu5FLZHAfopmvZTH5wgDdZyakJU2qhp+1gJagM8RpKNoLHIlPYVMpO1vKDqRx7buVDLwDHRoQJLki7yshy2eZ3ZexjIIJwWYL5Fx4LaXJWqlQzdDSwr1TT+WqZDTE5ppgOR6aGX5InnzkSaViYd/31Pm6Aw2+OgCMwCwFvHWZB4hbdQGBgwLZorkFWNdu4iiA7rmpxGVLcGYMx4Fwms3lsrFauMAkrZ5t8dP+K0c1YB6xjkcidYo3Oup8CD9ERcAR6GoGOW8WeRsUzlxSBWSt8CwU7RtfmK6Uqo6NhChbiZX2oOFnonGyIR9hOk6BhdGZBw8rr16/XxCjiWoor0zi9OC5DIhbkXBGwnQccYnUCXgrwPUxHoB8QcALuh1Je7jxSqxAP82yYXC3Dl6tHR40+Ya+KtLIJ0iOP8tBYBpwaGbZVxRCwGD2PVtquxIHL25x3O2VRBMvIds6U03QmZGEHMYXtt3gkShsyjk7nDMstHQFHwBGYCwEn4LlQcbtFI2CTkQIxptO1kZGheni2WUeyqy4z1zjwoEJlFQVqJyxOYiCsDEO1oRZzC3+Thd/KdbOwDgHzKMEXCVgbcSAAk5iORPpWcbq9I+AI9BcCXWyy+gs4z+38CFCxBvL5XCbFYO3woBGwESRrdjRXaX7Ps97Cf1zR2tYBV8oTY5uyGSZohclTLDpuchBddmxoDi2OASs0RGEMQSrmNIipVHUcl3t0BByB/kTACbg/y71ruQ4irfaqmhkmgiMjtdlsZpAp0RJe0UcnF4Il9cKwioAqCyNCkEiijL/aFQIn7C5ezQSsWWBxJxANPGcTzrfuYto8KEfAEegNBJyAe6Mct2AuGlVo1jjo4GChXC6Vi6V1a9biyNyV2aIj2ZUN9Gtc3mA8xpHXrTadMHIwWm4umLdSJWSWBSVWcbdKTS4bVuhVU7lsqlDIQ7pSQbMOGMbnkZ2+iDev9HVV+G6VJLd3BByBHkOg0Xr2WLY8O8uIQJA856hIhRz0ZGtkC2yUUR++rUuziVIXgiYSm2nND6InUFgQDubi0AcCxyJRmB071t7PErs7DsQ9OgKOgCMAAqGb70g4Al1EIByaAFMODOaNG1Op0eGhQKKJt8lQomxCskTMQLKwLwuTTPRkJ4xqRScxQPTJddvt5hnKr/O9zYU2X+jASUvoebQbiLtzBBwBR2AGAvWGcYatPzoCyRCYS/4cGRzKsSljtcwypMBV4cSkTlgrKJZr7DplF31GfizDlQSsk5GwnysJwcOib1HgJgWloERHJT5tCHiW+n3RcXoAjoAj0PsIOAH3fhkvaQ7n4NPAhNgzBsy4bLlcHB21ZbsmMHY0Vjp7ihe1lgFgSaUNAib0rg0Az0AsEjARFIsmDUPAytAMl/7oCDgCjkD7CDgBt4+Vu2yFwBy1KBDwYCadZvKwziIMBAmFJaPJetB25pBtTBXFXG2GhRA8WLBJWFyYkwbeKj8z7KWCxrJZApYbRT3DvT86Ao6AI9AOAnM0ne14czeOwPwIwJQIqciOkOJIOIswuI8EOr/vhd8ODTD7CtatMAtalB4IeGGPHbiIY8CshIpjwIQD+zoBd4Cne3EEHAEh4ATsNWHxCKgW1TgHSSOjoqWBHEcVVQZrZRTQRrz814TlJBFWJTHDgTU7m8jCD6HnCvlUljOIStlctWQ2WU4lStVynVEiwda7BiErzNxWlvhD2LzNEXuQr4vsyIXTsPkWZzPxM4/KcPir3oB/V6DilyPgCMyPgDcU8+PjbxdGIFAOFYk5VpNpDq2vnw+cWssZDHfdtutwfoC5S0xltrlTSZcLEXalkqpWNPUKFXDZTPDd8JrRYmVzKr1haKiGbpqTe9O1gumIk12kh5TTLwjztTEandp6YuaOkeRqNsWkqzUcLEHmaqmJcoozgIvp6siqUWZD237XuOfEYK7AwSS3wcUhHTxMew6WfnMEHAFHICDgBOwVoSsIQERwVMl+QVLkucBx9ZWJwQon2dclY1vDm+yC0YzUCMGIjP8NisvbGuNyLlPK52yLLHtL2HVTsjjwJAnYfDcWM4U9PWzRLz8+klzaJGA2pC5JJA8D0sbX+GkkKSawHj2v/HIEHAFHoDUCTsCtsfE3SRFo8KsxVio1MjQwOpjfcbt1kLJJioiQiLKLvBoMbgPMqXQ+nSnktBlVVCIvMoIp73H2NXFqN0qEccaAmQWtdcB1p41cT/l0kyPgCDgCbSBgqyn8cgS6gIAEUAsIFa5pdPfYdZuHH/7QPXe/5wjDtWhqJ8byBZMjk1xQ+SzODhw8PDTASC1yKkc+NEg5cPDUQ5J4WruVHGuMi3odFfSkbUDNzC9Shuhrl41Ph81H9Oh3R8ARcATaQ8AJuD2c3NX8CNgxR2HCUuA/o9tKauc1qccfd8S2IymO66WeMayaQpFr93b1LgQWeM4ChWwboq4pm4dZZFwpw4r5rE21NprEtk6J86e1k7dBAmY7aDuLUARMKLbuiUsEHEaPw7PfHAFHwBFoC4F2m8K2AnNHfY1AmGBlTAxXpoayRrrrRuzOD0rO54xHF3U1BTAyMJitVjmTYTCooG2UmLdLMOwaJWBSjgQ8BwEvKkvu2RFwBPoXASfg/i37ruS8iROhwHp14g+Ma4Q4aSrkjE1TYvpyprp5U9JI0WbLS13GRd0brIYG8kbt6dQA5x3iQumYlpqkUc3nPo4BcyaSxoBxXZeAp5nmC8TfOQKOgCPQjEC9dWu2crMjkAiBRh2C/TDW2bBcrLJD1aoBE4WrxQlNv8oMDycKWY7ZBGvKV2OWMoIva34LqRqbffC6Prbc5HDKy+JMYlkRMGYdBhwfFxe2+3YEHIG+RmAJWqy+xrO/Mi/KbcozFswdtko1XMgwOUrKZ9sUul7ROqhvTZOMLXgb5uUv8u/q4aFaqbz9urXYIAVXS5W6HMzzoi+OV7JB67DYlyW/o6Oj7KnJGHC4l9gIE8sC2eNibnTjrOLw7DdHwBFwBNpCQE1IW07dkSMwGwG4sH419M/hsT4RWmabeGWXduFIyMGonkPIgXjDeUphlvW2a1ZvvPvO0tiGoWyWSV6M/lZSlQyTvKYSFOLsxs14P3QDJAFjlgRcD9teLcHgczdS7mE4Ao7A1oyAE/DWXDorJW3i1xmpbVga+8JPDfZdJEHWd77IrFuVSlfKjAGPcOowMaerOqFoRiIW/2gdACTsIOOGdcB2IDBxJexHLD4hHoIj4Aj0GgJOwL1WosuenwbRWsSBlYyyEFWb7cW6iKc4SMhcrG6CwKdom2Bt0BcCHOE8hkKmkM1U4MRMhd0xWOpki4K6fRGiRN5yuapZ0BAwlnWxlxgRjZcg3m7nw8NzBByBrQuBhK3h1pV4T80WRyAQbdQwi5OmGBCGqpMU1FtL5zi8oOMUh7DqpE4MOdi8YnyYrtbi+uAKc627d9XVzg0JmEemQE9OTkLGbMRBGmwPTC6n3u5h7iE5An2FgEvAfVXcS5vZBtnOigX2RUQN1lqeNMtFCwvb3IONO5rf1jl4OJ8aHR6pjafZilJToKqVkoaDm10v3qxMKY5yuTxzHbDSZlOlp6Vy8fF6CI6AI9DzCDgB93wRL3UGp9GuKYiNi7jVmTJEbyuB9VxfL5QoUQ12Q9yWd0IvpFIPP+qITKm4/bogVsPwWY5D7CYNMp875k0qaNYgIQSTdo03T9GumfxyBBwBRyAZAk7AyfBy17MQmCb5NamYGxzc9L7p7axgWlngvXEqQpjHRRgcP2gE/MRH7cdJwxjWr9+4dvUQ9iwc4oykViHNad/QinPwYLzqIRALlvzoOqBmrqRzk7XsZCXHGRD5DCcP22HARvg45ymQNaPQWAQr3Zt7ITF8NzgCjoAjYAg4AXs9WAwCmttMCEZagXtiaPGV2fBatDbdTXTcwmCuLRwWGhkLhmCwo9aOctpSg/5G1qwK7zPJ2deCJWHh8EHO/7VFzKjKTZxmfW+5yFyr4VT+rmpq1ejo5lpurDq0qTo8XsyuHcxD/MOcEpzihOBM1nYFwVyBpm1sOuz91cToLXLn1o6AI9DfCDgB93f5dyH3ItZZAYk7G9bJeLfhy/4Gn9wCn9XjwjyL3lokozmoecymQhYX27QxTBatzeW2GV7ZWp6jkJhmXUxlirV8NZMrpNiGupK2XUHS1YxJwjnj8UqY+21rkS08C2FqFpos/O4IOAKOQERgcc1WDMYNjkDvIoD+mTFgZkFXKjXtBW3LkHzyc++WuOfMEVgeBJyAlwdnj2XlIRAplnXHM2ZBh404mKFtgm4Q0Vde7jzFjoAjsMURcALe4kXgCdjaETAFeFjqpHXAEHOhwBAw/Fsn4LrCeWvPh6fPEXAEti4EnIC3rvLw1Gw9CEQJOBJwXAesZUgczKDUOgFvPaXmKXEEVhACTsArqLA8qVsGAQi4MQZs64Ah5sY6YGfeLVMiHqsj0BsIOAH3Rjl6LpYWAUnD6Jwl9WpfjqWN0kN3BByBXkfACbjXS9jz1ykCGuLFN3Iug74DAwPsQ4kEjCJ61ap8lZVIeTsIsTxjr8xOo3N/joAj0G8IOAH3W4l7fjtBAAkYPmYrSu6Ym5cgNZs7Cdr9OAKOQL8i4ATcryXv+V4IgUizcRIWRyHhCf0zC5Ma+2NaKDjwyxFwBByBpAg4ASdFzN33FwJsUEmGWYaE7DsxMYEZ+tX5SwLCJeD+qhCeW0egewg4AXcPSw+ptxCQBKxZV0x7hoA3b95MFiFjCNgk4MY64N7Kt+fGEXAElgkBJ+BlAtqjWVkIIPZKyQzvknIRMBIwj0EDPZUb1z9PYeEmR8ARSIKAE3AStNxt3yAQaNdyKwKWClpjwEjG+mz0qm8g8Yw6Ao5AlxFwAu4yoB7cCkLAdthQco1STa3MSUiyROyt2bGHGc75NZtw3G+pUk3XqrlUjbOYsAyHGNaza2LyjMtC88sRcAQcgZYILAcBs3hjRvzaUxdLraqc8VZTXbCUx+hd8ocetSIzutHgnMLZuHFjDJABPMSUOJ3VRZaIjBuEgPiVA4BraYjVhnWzqTL8inHC1NAZFvvykawdHSyWKuMTxWpxct3IMH7TtSJvq5VyJXBvfVNKe9E4i9BONWSN8NQbx9wRcAQcgWYEloOAoT32Loh3os/n82xrAKFi4H711Vcff/zx++67L0x5r3vd66yzzrr99tuhYegZxzfccMN97nMf3N/3vvd95zvfiTLw73//OxsjYPj2t7+999574+uwww674IILxsbG7rzzzlWrVhEd1IsDhut4q6E7guJVc+bd7AiEw39FlQLDzgCWpQm54X8mVc2mrRtXS2eg11zGROMgEtfJdYpj68ppx9URcAQcgYURWA4CpuWCC7kzkwX5VbQKH8OpJHBkZOT666/ffffd3/WudyG8fuADHzj77LM/+MEP8nZwcPC222475JBDHvOYx/zqV7/68Ic//OY3v5n7TjvthMcLL7zwuOOOe8YznnHttdc+9alPfc5znnPzzTdvs80269evJyKoV/NXcTk0NETssDJczqNfjkAiBJiNpZ4cldZmYNGra/inXvkypAYY/tcRcASSIWD7+yTzkdA1LBgaqdhkpbChCdP97rvvXrt2LWnADVrlDRs2bLfddqeeeuoPfvCDyy67DDfnnnvuKaecokCI+VWvetX3v//93/zmN4Tw7Gc/m/unP/1p7PGIEHzQQQd98pOfVAKhW2gYM4EjZMPlmKF/LHE8PDxMnwAbJUNe/N6PCNTQEmc0RpKtlakjDPjW0tliKoNcO5Qqp2rVUjr/679seOn7/2dTYbt0cfyBu63+6MseNVqZpBYzcjyRLvCHvmQ2ZQobZOOyScj8x5offdzl6OaGqP3mCPQsArTeMxptWvI1a9as6PZ8yZsGExfCNn7ogUEQLuSijoiDYV/gwwHaY+6wLzS8ww47wJc4xg1c+5SnPEV16q677nriE5+IvnrTpk3w6I9//OM99tiDV4jUq1evPvHEEy+//HLoNi7WlC+CFRPziGZbln53BBZEIPYZkYA1nEHtok6qFYjeo7No4wZHwBFwBNpBYMkJGNYkHbAgnErLhWIZhTA2kChtGQY9oojGDQIx+uTTTz/95JNP1ijvX//6V7TKsDIu161bR38HIkfVzOMdd9zB2DDMDb+iu95+++1///vfI9Ei3ULe8oIzDASLM4ifKLDxyxFoHwFIF8eRgKlCImDZU7PbD8pdOgKOgCPQjMCSEzCsidz5pje9CR6l8YoXfIkZVr7uuuvgS2gV7oSGGdN9/OMfz4Auj5A0rwgBfTIMCpVCwDH1eNlll13E3FiOjo4iENMs4pi7Bpj/9re/MbS833778Za4NHgcQ3CDI9AmAnQWqa7UKwxcM3wZRfvlCDgCjkBCBGY2JQm9L+wc1kSx/NKXvpQZy2iPmcB8zTXXXHXVVb/+9a9vueWWiy66iLnNiBS0bkioTMXabbfdPvvZz+ILTTKsuddee+EGloU+kYkvueQSXO6zzz6w75577snMLPTPNItMb/7jH/+IJdIw7SN3+JvEwdCveMUr/vCHP/CKACH7hVPsLhwBWwQ87aLWcVHTsMVQf7fE8yempcAfHAFHoOcQsNkiS3rBmoQPB3Mpoh133BEDxMkaJIZ7NQ8LTfVRRx11+OGHf/7zn+ctvjSU+8AHPvD888+Hm5Foaf5g3AMOOACFM2Ixcu1vf/tbAmEUmTHgH/7wh8cee6xcEgI0jPQMtaPixq+kFqRwpcHvjsD8CIhjq7Uqki8uR0etQg6u3mZy03qUN0zWyjLFr1aqssY9m28Q8vxB+ltHwBFwBKYhsOQS8LTYmh4gToRUeJF5WDfddNMRRxwBMTP6y7oj5OMbb7wRe5yzPphlRa9//evh2i996UssUnrWs54F+8LBOMaGO4PBb3jDG6688kqmT8O1t956a1M8bnQEOkcgCruwsCRg7nZ1HqT7dAQcAUegjsCSS8CtkGZMF8Xy+Pg4A72//OUv0UsztQrdMrzLmLHkV3bV2HXXXbF/2MMe9p73vAft9Ote97qXvexlhIlGev/992e1EsptBpjRXUPGaLMhY4gcWdkbyVbIu30iBEztnEahMkXAVM5IwF7NEoHpjh0BR6AZgS1GwLAv6UBgRUX85Cc/Oa41gkG33XZbJVEbV+GSQVxs0FqjUha50ggiBB999NHMfJZjNIRwufx6syhM/N4tBCBgqpzqHpUwKo68pnULYQ/HEehDBGJLstx5RwKOcipTroge5TN3MShzphCOUVPDx0zFglyZ28xj9ML4LvTMI16Y2MWd1Uc4wKDQMPjlCHSMQJyEpTpmBwA3ZkFDwBZs0wys6Ljj6NyjI+AI9CECW4yAkWtp0RBqmUKliVrMbVEBwLVIxmJT8TEKZ9pBaJi2j2lWkDdeMEj+YHER4cC7+CIEPDoH92FV7m6Wxak6EpiQkXmRgDVroU7A2Jp2Omqjuxu/h+YIOAK9j8AWI2CgRcylLWMCMzzKuK8WDmEPAWOj5bw8IuDS9uESGRfSxRDV1xAtgrJKSc0ikjF+uWTpd0egMwSifCsJmO+ESoWZO7WRMGWvwF0C7gxk9+UI9DkCW5KANaYL77ISiUVKtGuItpSHGjjRMM0cAq4kD2RlZFzW+EK6EK1KjiFkBoORekXA8ivpuc+L1rPfFgKhqzbFoDX7Iph0Ff1iwond07VMrZxLlfJsEG0LhTN2ZmHjbQghG5YP108LDp7wNxVUDNMNjoAj4AiAwJZsHTTHCsrUiiNSI9FWGmmGfrGRLCtaRVZWmUG6olu5VDh65bKvcPB7RwiEzwECraWydjQwhylwLgMCb5UXuWwmly5XNt626w6rJytY5WxudLnEuYRMZWTbl4q+Jk4fqZaytSq+sZmi9o4S5J4cAUeghxHYkgTcw7B61lYIArZd2vSrPnjR+GPzrWTOIAGnKgUk4DQHfGmrLPt8pnMsLzhRGBHZQnb2nY6tPzkCjsA0BJyAp8HhD45ARKA+kSCMBsPBqGHQr3BhCKPAwaFZRB9ucAQcAUcgAQJbbB1wgjS6U0dgCyIQCJiOaiBgu9vVJN06AW/BwvGoHYEVjYBLwCu6+DzxS4hAXbJtSMBMOxD5miFGG+jXVc0RDzc4Ao5A+whMtSTt+3GXjkDPIxBo13JZC8dqYYB9Td/s5wH3fNl7Bh2B5ULACXi5kPZ4VhQCUwTcMEUCDhroaZlxCXgaHP7gCDgC7SHgBNweTu6qjxHQnhvIvmAQZODGtKsGN/cxNp51R8AR6BwBJ+DOsXOfPYxAfZ5zrZYpFCBdNitnq1T2bmP3mHXrCuwCk4WPAyVXKmHzjh7GwrPmCDgCS4OAE/DS4Oqh9gYCgWLJSkPmtVwZ8/ZG7jwXjoAjsEURcALeovB75Fs/AkHPDOM2jQEbAcdxX6mmt/58eAodAUdga0PACXhrKxFPz1aBwNTwbhMBkzJo2I4mVBr1ysXhraLEPBGOwMpDwAl45ZWZp3hZEWgiYIRdETAJMAnYCXhZS8IjcwR6DQEn4F4rUc9PVxAI3BpCmk7AcDASsF+OgCPgCCweASfgxWPoIfQyAvU1SGEBEvkMQnAjvw2WjuPBjRf+1xFwBByBhRHwvaAXxshd9C4CHGVkxxYxjJuFRUWk6WoqncEqdE6rLERi5Bc3uVQ5V5vIpssIwDjEG4ciZfiH3wBQvTObnvqmfHS4d2uO58wR6AICU41FFwLzIByBFYUAPFpiWhXkKvblDmema5XUZD4Fs9ZSrPHND7MImPMFtxnJF8obhgczA2kO+jUCrqUHatViOl3NVSqpbD74zXEnGF2E7BzcAMP/OgKOwEwE6r32mdb+7Aj0AQJGojq5l8zqwWyqQSwucvqvhFsRai5VydUmc6mqvhnzaCb+47MSjgG20PAjeg72ELdfjoAj4AjMjYAT8Ny4uG3fIAB98mu+7KOopdBOo4iWOby1M5AyuZxJzDPl2sZ+HQpl5tvmsN3sCDgCjkADAWtf/HIEHIEGAplAyMa11UDAtXSdnzMM7mbShXxeI76wLI5qqKu5wphxI4QZf2ew+4y3/ugIOAL9i4ATcP+WvedcCDR9AzKmkX0rYUTXHNRMsQyLprM5pkDncjkIOHpBTLZLEvA0yTfweHjpN0fAEXAE5kQgtiRzvnVLR6CXEaD2TyNN8poOmufAvkEFbZttMCaMS9uKspYayGXlxWzq2IiEexkoz5sj4AgsBQI+C3opUPUwVzQCEmuhVWNYUzFjtFVJafagzGfqvDuTuW3M2ATl4Lae/VluVjQsnnhHwBHoMgKNTnyXg/XgHIEVhQC0qatugE2nTLyBSrNphoPLOVshHGVf5OPosxFC+Mt35ew7DRF/cAQcgVkIOAHPgsQtHAGjzwaB1lKFIPWuGR1KlYvDBduJkl+5YtOzuMwtxjkuuHluep7DrVs5Ao5A/yHgBNx/Ze45nolA01fQYNKq1Mm1FIuPtElWdWJTZWIsn0lPsHlHKpUPY8HZnA3iVEqlmUH6syPgCDgCCyHQ1PQs5NTfOwK9iMAcnwAsnLUdsqomwdZsnyx2v9ph3ap773bPPXa7B8xbqbFfVpjnHAib2dGIwRoAboIoOGh6dqMj4Ag4As0I+CSsZjTc3K8IRFUxhsCp2ZTtAZ2qlFPVbCqfLqRS+++1W6X8sL122WEol6pVGPxl+0lTQiMjZ/L5CBysq4XC0cYNjoAj4AjMiYAT8JywuGX/IdCgXnIOr9Z3dIaMa+VUKc/k5x1GUg990N6rdUgDE7KgXvbfsFVK7NihidP9B5rn2BFwBBaBwBz6t0WE5l4dgZWJQJB6Zya9xshu2cZ7y0VWI9FXXZU26Ra3TIVmRw5zLyE4+IxS9Mxw/NkRcAQcgbkQcAKeCxW360MEGhwc+BWKrabKEDAa5TLci7wL9UpIZmVwNsOwb1ipxJ8WK5H6EELPsiPgCCRCwFXQieByx72GQL0HCuuKgIOGGdblr22ABbmiZ2Y+VjbDpzLYcBVQQP/MhpWcCFy3JQAYuqlL22TsNdg8P46AI9AFBJyAuwCiB7FCEYAy6xOmMIkuw/pf7XaVKYyEWdDhRUP5TE4bvNrYEytsDh0DEBQaKQ7mhvMVipEn2xFwBJYMASfgJYPWA14JCECc9WvKJGEY4gy7XjXs+TtrevM0cm04bAQYmTpauMERcAQcgSYEprUgTfZudAQcAUfAEXAEHIElRMAJeAnB9aAdAUfAEXAEHIFWCDgBt0LG7R0BR8ARcAQcgSVEwAl4CcH1oB0BR8ARcAQcgVYIOAG3QsbtHQFHwBFwBByBJUTACXgJwfWgHQFHwBFwBByBVgj0LAFXKpWsndyawpBhI4Vw2ak1bCi4alWxWAwnuaYwYDP/ZkabNm2SdzmbmJho9qJwmh3IvMx3JWPz5s1K3uTkJAkol8vLnIwZ0cUERAxnOFi2x1KppLImxvHxce5jY2PLFvucEUVMvF7Nic88ll6v5gGnV+sVzTiNOR+L2nPqANvBqklX68c33twazwPR1vOqZwk4ki4FxgXiFB4X9jwODQ3JweAguxulNmzY0KpI8DI6OsrbO++8U6UrL80lrfBxEw2tQls6e7ID+w4PDyt5AwMDZKq+X/HSxdo65NhNkQEM4T+ZW3tawjf5fL5QKEC9dE0o/fXr14+MjDQX4hLGPVfQXq/mQmVhO69X82PUw/WK1lVXMwLYqCWHffnGafFoZ7ia3WzN5t7fyZYaSQEgDVMqCD077LDDz3/+87322guiohWmUYarKDaJjLOLCi/bbLMNgeAdervrrrswrF27lvJW/0uNeJS21SObHc5S20Ata9asgYNJANS7bt261atX/+Mf/xAfL3Xss8OH5G677TawhedIFTCCHjTcCufZIXTX5u677yZJFBwp2bhx46677oqBotxSfRSvV52Vr9er+XHr1XpFs0ZLC91ioLHFgATMteOOO9IUq3mnMaehprVBxzk/SlvRW/UpevhOkXCRQVpbSbo0vrEAKDPM6kNFy2aDWFY2YtntttsuOoh0iyGa49vlNKj3J7qFiZcz6vnjImE4EGhbiu1IAP0A7pRRLGsV/fyJX7q3Xq8Wia3XqzkB7Id61dzSUg0uvvhiSDdSGN3raN76Db0vAcdqSmHQY7rhhhtoi5GEEMgoNnpV9BmlZI4umw2ovChjvMDfeIeDr7rqqgc/+MG33norLTjVXf0ymnWqBebmytEczlKbEXb/6Z/+idzRvcCMBExS6RuS66WOes7wBQhy584773xHuO5zn/vwd0tJ5PS96JdITwU+e+6551//+td73OMeGg+eMwtLaun1qjN4vV7Nj1sP1yuxKQ0sV2RWvmsJVKjWELRo1efHZ2t727METAlRTnPCTTnREMOmfMwqMAqvlTxEILTRqFIJEC8Y8IsvdNFwiUQ68a6qxZwxLoMl/QPSQ5eC7oJSdfvttyN3kuxliH12FIAGShAwCny6OGgOcYNhS30hqgzMT5EaHKwYgEBLv6Xw8Xo1u860Y+P1an6U+q1e0RrT9NHoqYnWZ04zuKXamflLZ/bbnj2MgYaVC4qdkWd6TDS7UibTW5RIRPmp5GY45pFAuCscfKHfoAmAcbGMXjDIrPvsQJbBRnRCzSORGqKGfUnnbASWITFEwQdAkgAEuRPBF9ojVdIDL08CZsSicpeq4+abb952223BipqwpYrM69WMAmrz0evV/ED1W71CcIqyE3IUzR3ix0phX4pyJj/NX7or7q2qI3eoSGbaXMoJEiUviEGi0nnyhdQr6Y32GmcM76sRJzQFiGWzeZ6glvQVeYFj0DzDc2SKLgXS3pZiX+UU6gUZpF4MpAokF0R76SCi3Alc5b799tvTJ9hpp53QhSxdjPOH7PVqfnzmeev1ah5w+qFeNbe3GhkEENpn2kDIGLFqHny2tlc9S8BwD5fkG+7RTAGgrBCnysydt60KBiZDSuaiVwVzS4n6hje8ASamq0VVwCPtOFHE5XetguqKPSlROLGeoQyH2MjR6173Oj4/3uqOuLk8hIeeWUkiXr4HmZVOkCFVpA3cgEvFAWgkDDBjFqJBfhdzV4koGaSHeBWa7HXnQ33b294GGaMtEESRifWoztZiktHsV6ERhehfcVGv9ChMKEQq2Kte9SoeNYLAWzDkUaXZHOBSmJUYQkbHw51Exvp81llnMWqOJRnhogMqGJciGc1hUkP0SKEQo4pG9YpHvkFSgpYFuPQKx+RCaeMey7Q5zI7NlEKMhUAUi0LTBHsSRut/xhlnoF+Rgk1ulODlKUTSQ73iTjFJCgQQDKeeeip31SvekhGlJyKsjCzFnexH3PjMVShY0jq9/vWvV2XTJ0CS2iwy3MsLCQZz5Uv5xSYaliI7XQ+zZ8eAu4UUzRCfExRC5dB3RUWBsBGh0PGqHlCNqPc061R3XHYr6vnDIRkSLuWMb4mORfRCw0QVj59ctF86gxTOEjT50ogacIRYhI4PD6BIeUyYxPSl+GYUl/JL2ogiRh1f0TSQSB6lxcJACSoLXQSKWJp7eMRC9hmhRxCPKQEiOg2qPKBH2x0r0rJVqthqq8UkzeCGuKlEkipqOLBQ64BLDX0XUZoRlBiUNJAYalFscHHWDBr2wpYeDPVfFUnDDTMC7MqjkCEoGWLC9PWRZqUTJEmV4IqkguPooCuJaRWISiqWF58YPRVVMNKJLgqPEcNWgXTLHqCAgti5Aw7fPpUKRR3hq+BIHvbUdj12K96VEk5LyW+lZGCp00ndFVtQoRUXO3JQV2g9qTdQLzUMaZivi8odG80lTZUqMamicSRVdMCpxGJfGnHaR74uXuEgfvxLl54oYhKjqAtqITGgocaI1AKOEhAFUz5CPJI84O0u+xIdzRzRETgXUGAmbSQGMwWHpQpUrST2eoTnIBVl4e9//3u3EAMHrpgSglWrTf3BTHokBGCpCgZENNxUJOoVr5aNfUkMBQF6pJZUcWEDbtyBhfSLcZWRpWZfIuVSuVBAXLIR7ZE20omNQKPyY6ZNJ/0kHrMKkS6yfHXlTkFQrwIwRiREJDZVLPRI1BRgiYGUUIgUJWVKInXxSrWxK+lpFQhREHX8CnCmSEkzlUrjU4IxvmoVVFfsQQzoCIoEgABlyvQLwQhQtF1qATDjhqa1K5GuoEBcAm6rsKjQVB2qMh+e2in4gyql7iRfGl8gr5pl0LbCTe6ID54ajD91bGMApI2KrqaH2kyfAJvlaShJg0QlDHzbfF0xVTwCC1++Jo1joAWnPYoOMGDDJRm02b5jMzGSBrJPSREI8hDlEouGb55SU5FJ5mNFGcv5SQMJo1jRHHQc9TweFT53iokoKCPqD8VEG02LKY9KD6BxiX7ka+lEuphg2j7mq/Ooqo5BEGEghZipWkBKmpWw6HGJDCBAIYrkiAIcuFNAqtu8pRyVYBXZTTfdxKIykoczfRoi6S6mlkgpO76pGXVViRRcuCENXGBFmlWyVEjcxFJeIsQUrPDRndQSr74CveUzpPKTPB4jtkuaHlVgoojVSZU8RhrbseggvuoHgxPwAqUsGpvRLlOz4RLuvKVhio3XMtQhpSe2R6Sez5svioammflIMM2E7BfI4eJex++H75mQ+LpgFwiDqGkuSRXJ4LOX0klR0TKSPNojkWJs8ReXkLrvGZ+3kkQbJA6LcQk00kaysaRVBVLSzB1zFwkvNkDz5I7mkuokqohViITBfCSmixQyTxp4paqFQXIJ8UYwZVC1xxn9Le2sMn+Ai3kr3CgmVZIZH6BC1kK75ljih8m3gMfmzk2zsw7MsVzkV6WDJQIcn7/0GX/5y1923313HMSPAjO1C2eUL7VLyesg9sV4ATqKjwSQVIVDeihEPkDgbabnxcTSyi/liMzA/oM44LOiULhID58eNtQxCpG00WhE0aJVUD1p7wS8QLHGLx+1JPNm4RK+KL43ffyRevm09M0vEFw3XkdxU/1cBclHxedEPaZpoBtOIqnW3YitrTCIlPYFpxi4mrsC0T+pokHkg0Tsw5JHOYvtfnS5GINCU6vHR44hSh7qB0TqVeOuZICeWgRFTRaUncWkRH6pP7Q4hEZ3JJIxDCeteGypY9s0O0YcgxjeZ7/qrg1QgEOES/gQBRiqExDT39145wlNDEpxEDVp0NcnLlEZkTaQAV4hiXsqlcq9uejniaLNV7G6AhFRCBD8KhkkjwRQptEZryhTnJE8KsCMCtZmpB04U5tA1KQEHGbwq8qU1BLyMtQopZ9I+Qqkrmj+sjCTWint5LL5bQd5X4leprSFKzH1y5BmKI02ne9H2uYPf/jDj3rUo6jZj3zkI7/3ve9Rq/jmSQaNQnMLvqQJQyoifL4xMdmrX/1qlG+77LLL/e9/fyasioHEvtT7JU0JgdOZ5XumBfzmN78JJkgAfNgXXHABr+gogBuGSy+99MQTTwTAe97znk9+8pN/8YtfKFW0WXycc7K1HHR8Jz00PTR/b33rWw8//HCiQGJ77nOfy05htIYqKQwkgHbqmc98JuZPfvKTik7F3XHUMzxSf9QKY08vjYtC+clPfrLvvvsS6b3vfe9DDjnk61//Os0QcP3Hf/wHW3SReGB83vOed/311+OLxC9DWwldEZGQAZaXv/zl7Fz2b//2bySA9NPdJG2oMUgbJXjjjTdivwwXUSsWDEBHGYHbG9/4RlU55hs/61nPEmJ8lZ/97GfpPfBd4AX3lGwXUwg40IM+KAKn5830ftBQ6YDbkUceybdA5aFBuOKKKx796EeTMIY2nvCEJ4CngAXGLiZpzqBoE37/+99TfEcccQRjVfe6171+9atfiXHpyVHlyMUrX/lKivI1r3nNnCF015J6RUE89rGPBSsiBS6B9oIXvABYaLv23ntvvhFasGc/+9kUcXdj3/pDcwJeoIyouxAe3w9f3X//93+zzOCFL3zhJZdc8rCHPezxj388yzOkyVHfjfsCwXXjtWQU9Rzf/OY3f/rTn/7EJz7Bhqhvf/vbTzvttE996lOkmYaAqKjZ3YhwvjDgOeiBGMHnmGOOec973oMBDyQAgYBX7NxJGwTTfPvb3/7Nb34Dr3ASBg74MtGJ0SLwfSq180XT9jv1ToiX8EnVH/7wh2c84xm/+93vfvCDHxDRYYcdRjNEYLSSdA5IwHvf+96//e1vtAtoNfDCRQikqu0IF3BIlSAuZZBaRMeIQqEd/9CHPkS/hHNBaCif9rSnXXnllbfccgs6zPe9730MZ8IlP/3pT1mnQXpIpPoxC8S0uNeUGukkOoK5/PLLL7roIhJJh4n0wytnnnnmeeedRx8LaqHOw8GLi21h36I6ygWnQo+KBGiawmPlVC7TcyIx//Vf/4V26uijj6ZNp2+n/Wcoa2pmF79HigDmoOxUwShKdkDcZ599iJqCo5qBGIVLzbnsssv++Z//+eEPf/i3vvUt0kz1A1tlAQZaOOeLc4Ei90lPehJFec455/z617+mcdhtt92EIT1gYPlGuEjq/e53PzBcXGwL+ybvxPWlL32JLguVnG+NOk+5PPGJT6THCXTvfOc7+Typ8DQOT33qUxcOscdcgIVf8yNAvwwH1F0+cr55OaaKU7Pf//73U42woXpx5zPT2+W5kzA+NoQARUffH/NTnvIUiAcbOpjLkwwojYhEMxj4QGis+bYxA8hJJ5309Kc/HTOpwg0GIRYdYNPdS+U1O/soMEnbL3/5S6KDVLjDNAwr0CjQm/nqV79K44UlTb/S1sVUqWJQhZQqDASudIIJPAe1xMpD7DT3tFn0YHCGg66nZ3bWiEKpQiuIUELbjUIFUQmXaCno0ICPEkwzCg/9+Mc/5lVzwvBOsmeH3JmNEoNfYCEW6hhagR/+8IfHHXccmgyFCT2TKhUlNhAMMCqReCcjnUU9jy99WXJA9+jAAw+UmUTyinRSWEcddRSrzLEnJdhwYe4iMoqx1Z1UoVOhDuMg1ig5JoVUdTqa1157LRInbVdzdloF2C17EqOiefGLX4xuYDYgVHg+T5zFoifqZnO3UrJVheMS8MIdKkmczJXl+4fh4A/5Qct0/vnn09HmG+Pjp52S8LdwiItwQQXVRRgkjG4sHW2ptvi6vvjFL8J2tPK8pZXE5SKiatcrFRqn9P35nvnGEA4QUySDIjR85zvf4Wt/xCMegfKQHszHP/5x2I7PDwf4AjFww2O7kbXhjkhpr8k+bmmF5YMY0T9TTMRFjPTKAe1xj3scCgPGEei78AoDeZFM30Y8bTkhXtypYkiE4hEDZSTdyZe//GUK7thjj4VOVF68QswCH/VUcC+s2opvEY5IFcg85znPARY0FqhPlXhEFkCjtpNgkkSVQyj/7W9/y1vc00QSJ7jhXdLhIpIw5ZXQ0ATwDHSY0fQgMz3kIQ/hjEv6Jdjz0aF2RvcDbnDw//zP/5CYf/3Xf+UVhY4XEombqRC7YSKbxCV6I4prrrmGDhy1/eSTTya1FBMSOaIwzg444AA66Og2AIpHvgvih567kYr5wqAFeOhDH/qiF70I3B74wAeee+65ag0oJoBCfnjHO96B7L4MyvCYSioPBUF6SACdYCRwulBUlUiuuKQuAQ4foIpbfsGNC2dcMbReMyiTfm+FADWDCsQdXROtNromHtWV+3//7/+h0sQjXz53Wk+ctQqnW/bURYKCMLjzFXFHh6NKSf0+/fTT1WrDQNR13i71Ra6JIkohQEFikEuw5BWKAR4Zl2Jwmnb8LW95CxpCCU844LNU8rqIm8pCsQsrykuxwCtIJ4IOYZftgRhN4BVegO7CCy8EOqHXReGpOY9KCVhh0PguR0VxoZzHmVJL1eItSaIxeu1rX6uUq8WXeenuJACNJWOZggjGFT404pQaKRQ4JAB2YdMu0qnSx0ZfhLLQlRTGcqQm00lCKId6CZmeHGynAqL+I+1RwejzQYeUIGSDG1JFay5DVxKjQEhSzC82jEN95CMfYTic4kMUZrSVVP3v//4v1EKZfuELX0BVgK6e7gI9P0HaRXxa5YtuB1OO0Xuj/UYRRexod3FMyhltYbwDQYJHEkzilyE9xEXdiF8BLQPlRY9TLYZaMKo30DGLhRE03JMqqlb8Injkwr4nL+u6+rUgAtQAmmyqDvKKHPOp082ksxlbCuxjPVswwMU4UK0lBFJF20QHHEGckSe+N4TLr3zlK7GZ0Ge/mLgW9Kvs64ORY1BisJB2k0f6K7TdNAeYBQ7DY8cffzyPAIgveenuBxZ7AxiUDGL593//d5pIaA8zlvQGaKcYghJWyC6f+9znliIxCpM7lEB+yakKhTuJQdOLToWUMLaKG0GEPerfxzzmMdiI2GIgS2egUsElTLxigpjYi1HMl7zkJWAIl9BBiVHTqaLaSzsdLYV5F+u/6gakSxnBFgwTCjf0KPQyiZdOGz1ghLwf/ehHDCXQWUE01/iCaFhuYgoXb6AsYkWFM5RZ6hJJ5aIKMewKK1P/X/GKV8Q0IAczXYvYsWluKxafnjlDIPZDDz1Ur2C1U045BcR45EtEJwSYmMkFX6X05HMG0l1L1WEN0jEdhNkzzeGTGBw84AEPQAmkrxUw+Sq7WJeao9vazE7AC5eI2iO6/1Ruetl4kA3NExKV/NM6LMPXFdNKF5I6St3l8/7MZz4je743RoCQFSIDRfdLaohtDUoCQKAlQh8ooZZXgKYWU6Ax8vSgBz2I9Ij5cCD7LqaQT1eBKw2Y+ewZZ0VhqK+awqJPwGAY2maSJ8Up/Zj9999fHmM725VUkYzZrQm5llxLc3PCCSdI1iR2iJBRc9pQkOEVzub03pWEzQhEc9cBBBUlyh4M9OegXjQWjC+o20d6yAtU9+53vxvvkZOWAjclT0ODJEYzmFCDY+YS1f3pT38SSjhmujv6c9VGvgWV/ow8dvxILKoVfFwzFBL63OglwLscMo0SFT0wEYEVyWBOBgWqeLtbr+bMCyPlJINXVHIu5o1zTDjxfuADH6AowQ3q5Y6gjJjOJzBnIN21VOWnqlBYFB8qaMJXMWGgKaOLwNxMieaKmgRzdTcZW2doPgZsH/P8F18yDug/ov6ikaLq0B7xdVG5NR2Uj1Mrgijj+YNa/FsYjkD4eDRYwhcO7VFZaQH5qNRIQYG4UbIXH+OCIcQ2kVFDUgUaXGI1HtFk/vnPf+YjJIUEhaCJ/MQjr3gESa4Fo2jfAVCQfQKnlVQaUFQwu5jxe8Y1UVSCGIWFOu673/0uAtMf//hHlm0QPiLU97//fUEnkNuPdH6XJIP8NruhmSbXkBxNJM0ibRCpwgGP1DE0lqSN+kabhTO8k+xm70thpj4TNVCgOGVZFHOw0VWwrgx87nvf+wIIi+6AlPRQmohTvCUZwEXaqPYqze4mjPFUAoTAKCZEYTS9tOBMPGaWA/oeap1KGQBJFekHKHQJ4MYXwbeg+tatJJFHBUi86rdpTh/1jXJEsqSDjv6AeBk7QP1LvCDDI86YA6Hy5bvoVnpahUPX7Wtf+xpvqeRcV199NeCQEkajUbdQuIgQoEd26Bag7WgVTrfsqflUEjIObvRLwIq1EgROMXGniFl9RKvFyDRlR8EpXhLMhdmaktD1kX0P3ikJv+ZBgA87vv3Yxz5GXWFMhWqNrEltQFtI28TXJTfUtuh46QwaDOPLJ14m7tMk8clRd1mjDDEzyVBRx1QtXUoIWbHwkdAG0e4gLSFK8jmxLIppKTigNQcoVPdgxSRVAMQNbRmvoqSO9y4mEhojNMqF+8te9jK+ZBodEnPddddJYai4NKIJyfFICpHaMehxhogj953dlVMFS5eInFKjWKwFLMiULL1AjwomzKRjjOOwww5jnhEsiBk8oTp5XIZ6RXVSVVeBklRUhSwVpYkk4y996UtRD9BBYVEp/ScSiQPs8cVdyRPgnaE025eqBCFjIBbpTomCeXxonhQjqhR09fQV4BUgZRrd5z//+Vh2+OpuvWrOIJXnXe96FyOadFBoDQ4++GDGoeFaYoRm6ArQO0fjouOt6FHNzuAS2TDni/7Bm970JmJnhhq0R+MgHHRX+dJQkP4lSkNzsCop7nyVKE6aRy4oU6bR0XxRz+kiS5CgTZAXAiHBVDO+IFWz5mB7xuwq6IWLkoYpfnuaQ0i1pt5I/SX/amcXDqtLLkRdJIx4mUzE58T3T5I4o40mW40jUalJ7VKcLYMR4ak3LTWXeq+sy6SvQFJpAtBqkkIacYarCSimEDPf21J8YGpuYFYJkRgkuDBQR6Qq0Bg1HXNG0MV2LfPZ0YtmmlclIVKED6QlsEJheMQRRxA1YUNvJJIrSpO04wjHkVE6ij+BJ0ohphBvELAU45QvrTYzV5FRSBKylLpW2Asx1calQG926kkVvQHsYTuIjWV4yJeIeswL++AHPyj3SklzHZsdTgc2Aid+U6gH0A0g41J50LLQvaNkBQWtBOWLPZ0V+gfyqEHQDuJN5AX9HDGy1J7aDuF99KMfjTjERows7LHHHgwGRZtEUSR1rC+RoXrqNv0VvAsQ2dAsYM9HalU/lUIRHbXTIuClaBySZmHp3Jv6SDn3uyPgCDgCjoAj4AgsGwI+BrxsUHtEjoAj4Ag4Ao7AFAJOwFNYuMkRcAQcAUfAEVg2BJyAlw1qj8gRcAQcAUfAEZhCwAl4Cgs3OQKOgCPgCDgCy4aAE/CyQe0ROQKOgCPgCDgCUwg4AU9h4SZHwBFwBBwBR2DZEHACXjaoPSJHwBFwBBwBR2AKASfgKSzc5AgsGwIcdcDuDYqOrQYwsIcDdzZJ4N68Op8dJ7BhOw7u7FHAXRdbfDSMU77wyMYLsmcfA3b+kpmtUTghI7rHDbtVxEdFyiObc3HXBghKQ0wJmyfoVbSJ3t3gCDgCnSHgBNwZbu7LEVgUApwxwO6JbEXEGU3sIQr7snURO1CyyRrhQrfiUViWjSrZYkl752rDIPalwo2OxcUlVCpfbE0F0bK10Bvf+EYOn2APXjZjwrFCY+NGfIlrIXJe8ciu1NzxrjDZqV+9AQIhKLhf3QJCZn8uqJcwI8Hj0S9HwBFYDAK2Ib5fjoAjsMwIsK2jREkkS1iNiwSwjTbSJ1zItsY8QntiWW1OCfNhwBKqhibZyZLdDdn6H5fQJJSJYwxcbM3I7qTYQ9jibMxyCdfC+jITEadp8QqGxgvhEwjBQs9EQcLgft7GWDCTPAJEsFYKsfHLEXAEOkbAJeCOoXOPjkDnCLCTMDsts5Uxp/ByaCt8icQJd8LEbO7PWUM8ckjq85//fHbHJRqYb7/99uNkWfbZhzXZkBn+5oy5PffcE9Z88IMfzDbgiMsQJ2fCc8DD//3f/xECjx//+MdhUHiX8x5E+ezXz8FHRASJciwjvAvpwujs9vwv//Ivr371qznAjpOjOEgAJka8hq2R0RGpcYaZ8z+cfTsvePfpCDQh4ATcBIYbHYHlQgDCg1MhNhj07LPP5oQ4xEoo8+abb+YAR+iZY4gYtcWe05SRUOFLHHBkExSI/VlnnYUojDCKKpsN7t/ylrdw4sUZZ5xB8jnx4uSTT+bO8TKEBsXiBalXSmxOd3jWs57FQbCca/Tzn/8cMuZITdTRkCtRcCwEnM3Bf2eeeSYnC3HUNK9ww3A1e/dzZA2n8KLKJrTlwsnjcQR6GQFXQfdy6XretloE4NSddtoJpTFSKWSMfImsCdtxLPFxxx132mmnSRqGaB//+MfzCDdz3DKCKWcBRQEUgZgMokk+8cQTOY0HeuaRg27kAPcMHmvyFBHpRD/ORUbC5mhYjvHBMbIyB+7CyrvssguPSNIc5446GpblWD1ImuNaIXIE6Cc84QmkgVQhGdN1wLFfjoAjsEgEnIAXCaB7dwQ6QYDhVXTLMC4aYDgSARQJFZUvs6KQeqE66BlKXr16NYplLmgVCXjvvfeGXHmEPhFYv/KVrzCTi/ODYURmVxEIScGMZIwgC8cTJoHjHnEZjzjg6Fz023jHGSFwXh7c/Ic//IH00BXADR71imP+Lr/8clIIK3PuHmL0Yx/72MMPPxwVNAzdSZ7djyPgCExHwFXQ0/HwJ0dgWRCAXDW1CiEVlkUI5oIXETcRZxFJOV8ZafWiiy6SeKoZUgjKyKBQJr4uvvhitMfHHHMMciqaYTTV0C32XASFM2gYMwyKe3EzlvAxY8Y4UC7RRUPPECrOsEHShZW5iA4bDFhCyTDxl7/8ZeaIIZEfeeSR6KKXBSSPxBHocQRcAu7xAvbsbZ0IwL6IuaQNkkPihCnhRdjxoIMOQpnMnCwu2cOjyKY4RgKGR0WKvGKaFWIxo7/QKhzJZC5EXgULdyIQY4ZHiQLHXASCM2ZyfexjH2MQFzfcL7zwQgzEhRkHChydNpRMkggQ7/l8nrcMS3O9/e1vh+Z/97vf7b777lsnsJ4qR2AFIeAS8AoqLE9q7yCAthlmJT9MOWbeMgzHXCqojiW8qKBf9rKXMUyLjvqCCy449dRToU9c4h5alS+olIVMN9xww9e//nU8fuELX/jUpz6FpIszZGvU10jSiMVa5itaxSN8jAKZsWGUyVdddRWiM2PAmJlWzVvCRDKGhpGDSQ+UjxaaR6Z3nXPOOVdccQWzsTAgB+O+d0rCc+IIbDkEnIC3HPYecx8jwJir1MLvfve7ESjRQjNvGbbbZ599vvrVrzJPCnET/uPtrrvuCi9Cxkil6Kgx33bbbSDHEia4mblRJ5100rnnnsu8ZegZe9ygxD766KN5xZjxe9/7XrxoRBmCR349//zzie5+97vfCSeccPDBB7/vfe/DF3tgIX9D1Wy+gTP4GF833XQTNAwfQ/OHHnroIYccgl9mhJFUvPjlCDgCi0QgzTe/yCDcuyPgCCRFABEThtNwLISHd82Z4nuUahobCBXyY/YyKmIeEZohV0WkzxaXCMcScPWWO+Eg42IPwUcHCMRwsPyiW0Y7Dd0qXimceYWNNNikjTAJijlZmCVzy6+SpBBk43dHwBHoGAEn4I6hc4+OwGIRgPNEwxggY4KD9iA/qBF7LggYS83Y0gAwLnEALzJ2G6PXMDCvxKmasSWylHccM9kKPkb2ZUY0RE5oOMAlNtx5hH2hbTGuSB2ixYYwdScovMDH0D/uY+xucAQcgc4Q8K+oM9zclyPQBQTgNvgMmRX2hfkIEWqELGE+bLiYkIUlBu6QIpQsssQNpAsvsl8Hrxi+5U4Ikoy5EwLkDV9iLx5lhBiu5RV30SehYcYBYcK+GIiC9Ih9ecSSRxyTTqLjIkxY3NkXcPxyBBaPgEvAi8fQQ3AEEiMA5yHXatqUPENv4lHtvQx3wnNQL3ItnAcXMgsanTCOpW1ujpLQorgsORgb/BKIZGg9RhEZAza8gmulgpbgG8MUc8svEjPDz6SBt8RCd4HH6NINjoAj0DECLgF3DJ17dAQ6RwB2hH0hQiiNUCA52Bfa45EtLyA5yA83POJG4im0JwEX8Rd7CFt+MeAS90i6BIW0ihcJqVhixhIHml2lEPDCbCxIFy4nWB7xhTM5xg0euZCwsYR6uSSg84h70TNmvxwBR2AxCLgEvBj03K8j4Ag4Ao6AI9AhAi4Bdwice3MEHAFHwBFwBBaDgBPwYtBzv46AI+AIOAKOQIcIOAF3CJx7cwQcAUfAEXAEFoOAE/Bi0HO/joAj4Ag4Ao5Ahwg4AXcInHtzBBwBR8ARcAQWg4AT8GLQc7+OgCPgCDgCjkCHCDgBdwice3MEHAFHwBFwBBaDwP8HDc2miBbkP8IAAAAASUVORK5CYII=", "text/plain": [ - "" + "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.convergence_graph" + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(nrows=1, ncols=1)\n", + "axes.plot(cost_values)\n", + "axes.set_xlabel(\"Iterations\")\n", + "axes.set_ylabel(\"Cost\")\n", + "axes.set_title(\"Cost convergence\")" ] }, { @@ -463,22 +350,22 @@ "id": "670eddd3-2da7-4a88-b571-7884ef24f60c", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm:" + "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the pyomo to qmod translator." + ] + }, + { + "cell_type": "markdown", + "id": "c99a7e3e-5203-4893-b970-dc23739f6df2", + "metadata": {}, + "source": [ + "In order to get samples with the optimized parameters, we call the `get_results` method:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "516d78ba-2951-46eb-b1af-efe877513556", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:33.675397Z", - "iopub.status.busy": "2024-05-07T16:03:33.674963Z", - "iopub.status.idle": "2024-05-07T16:03:37.107224Z", - "shell.execute_reply": "2024-05-07T16:03:37.106517Z" - }, - "tags": [] - }, + "execution_count": 10, + "id": "9638f749-a60b-4176-a4ea-50d7c2bb986f", + "metadata": {}, "outputs": [ { "data": { @@ -501,79 +388,63 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", - " 6\n", - " 0.019\n", - " 3.0\n", - " [0, 0, 1]\n", - " 19\n", + " 151\n", + " {'x_0': 0, 'x_1': 0, 'x_2': 1}\n", + " 0.001953\n", + " -3.000000e+00\n", " \n", " \n", - " 15\n", - " 0.015\n", - " 3.0\n", - " [0, 0, 1]\n", - " 15\n", + " 178\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0}\n", + " 0.000977\n", + " -2.000000e+00\n", " \n", " \n", - " 24\n", - " 0.011\n", - " 2.0\n", - " [0, 1, 0]\n", - " 11\n", + " 11\n", + " {'x_0': 1, 'x_1': 0, 'x_2': 0}\n", + " 0.014648\n", + " -1.000000e+00\n", " \n", " \n", - " 106\n", - " 0.002\n", - " 2.0\n", - " [0, 0, 2]\n", - " 2\n", + " 4\n", + " {'x_0': 0, 'x_1': 0, 'x_2': 0}\n", + " 0.020020\n", + " 1.527468e-150\n", " \n", " \n", - " 3\n", - " 0.025\n", - " 2.0\n", - " [0, 1, 0]\n", - " 25\n", + " 68\n", + " {'x_0': 0, 'x_1': 0, 'x_2': 1}\n", + " 0.004395\n", + " 7.000000e+00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "6 0.019 3.0 [0, 0, 1] 19\n", - "15 0.015 3.0 [0, 0, 1] 15\n", - "24 0.011 2.0 [0, 1, 0] 11\n", - "106 0.002 2.0 [0, 0, 2] 2\n", - "3 0.025 2.0 [0, 1, 0] 25" + " solution probability cost\n", + "151 {'x_0': 0, 'x_1': 0, 'x_2': 1} 0.001953 -3.000000e+00\n", + "178 {'x_0': 0, 'x_1': 1, 'x_2': 0} 0.000977 -2.000000e+00\n", + "11 {'x_0': 1, 'x_1': 0, 'x_2': 0} 0.014648 -1.000000e+00\n", + "4 {'x_0': 0, 'x_1': 0, 'x_2': 0} 0.020020 1.527468e-150\n", + "68 {'x_0': 0, 'x_1': 0, 'x_2': 1} 0.004395 7.000000e+00" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " ilp_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)" + "optimization_result = combi.get_results()\n", + "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { @@ -586,31 +457,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:37.110324Z", - "iopub.status.busy": "2024-05-07T16:03:37.109906Z", - "iopub.status.idle": "2024-05-07T16:03:37.306818Z", - "shell.execute_reply": "2024-05-07T16:03:37.306110Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/plain": [ - "array([[]], dtype=object)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -620,7 +475,12 @@ } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + ")\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" ] }, { @@ -633,31 +493,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:37.309483Z", - "iopub.status.busy": "2024-05-07T16:03:37.308945Z", - "iopub.status.idle": "2024-05-07T16:03:37.313874Z", - "shell.execute_reply": "2024-05-07T16:03:37.313442Z" - }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ - "[0, 0, 1]" + "{'x_0': 0, 'x_1': 0, 'x_2': 1}" ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "best_solution = optimization_result.solution[optimization_result.cost.idxmax()]\n", + "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", "best_solution" ] }, @@ -679,15 +533,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:37.316362Z", - "iopub.status.busy": "2024-05-07T16:03:37.315830Z", - "iopub.status.idle": "2024-05-07T16:03:37.375955Z", - "shell.execute_reply": "2024-05-07T16:03:37.375336Z" - }, "pycharm": { "name": "#%%\n" }, @@ -748,7 +596,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod index efe08cc4b..382226df1 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod @@ -1,1079 +1,20 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=152.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-11.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-11.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-25.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-25.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-50.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-24.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-24.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-49.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-25.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-25.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-51.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=5.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=10.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=10.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=10.0 - } -]; - -qfunc main(params_list: real[6], output target: qbit[11]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + x_0: qnum<2, False, 0>; + x_1: qnum<2, False, 0>; + x_2: qnum<2, False, 0>; + monotone_rule_1_slack_var_0: qbit; + monotone_rule_2_slack_var_0: qbit; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=True, -initial_point=[0.0, 0.03762827822120867, 0.018814139110604335, 0.018814139110604335, 0.03762827822120867, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=90, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[6], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 3) { + phase (-(((((((((10 * v.x_0) * v.x_1) + ((10 * v.x_0) * v.x_2)) - v.x_0) + ((10 * v.x_1) * v.x_2)) - (2 * v.x_1)) - (3 * v.x_2)) + (10 * (((((v.monotone_rule_1_slack_var_0 + v.x_0) + v.x_1) + v.x_2) - 1.0) ** 2))) + (10 * (((((v.monotone_rule_2_slack_var_0 + v.x_0) + v.x_1) + v.x_2) - 1.0) ** 2))), params[i]); + apply_to_all(lambda(q) { + RX(params[3 + i], q); + }, v); + } +} diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json index 0967ef424..af12aaf3f 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "tdg", + "ry", + "rz", + "s", + "r", + "sx", + "t", + "cy", + "p", + "u", + "h", + "cx", + "sdg", + "z", + "x", + "cz", + "u1", + "u2", + "rx", + "sxdg", + "y", + "id" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 2268737765 + } +} diff --git a/applications/optimization/max_clique/max_clique.ipynb b/applications/optimization/max_clique/max_clique.ipynb index f4d1fba38..d8af81f07 100644 --- a/applications/optimization/max_clique/max_clique.ipynb +++ b/applications/optimization/max_clique/max_clique.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "13d83b59-57f7-48ee-8bff-deda7d28edc5", + "id": "10115924-81df-4877-9ef5-0bd2c7f21980", "metadata": { "tags": [] }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "6d56bb6d-9f3b-45db-8e98-b50f27af7505", + "id": "b6916fad-2b36-4cc7-9f4a-b9991bdcdc29", "metadata": { "tags": [] }, @@ -28,7 +28,7 @@ }, { "cell_type": "markdown", - "id": "3b5fbdb8-373b-4629-8ca1-51ef9be82edd", + "id": "cdc179ce-71f3-4269-a128-94b42b4e00a8", "metadata": {}, "source": [ "# Solving the problem with classiq" @@ -36,7 +36,7 @@ }, { "cell_type": "markdown", - "id": "0bdc4e6a-199b-44b3-bd3f-fd24722b616b", + "id": "43c06afd-0219-4c66-bbdf-8eae31ffd1bc", "metadata": {}, "source": [ "## Define the optimization problem\n", @@ -47,14 +47,8 @@ { "cell_type": "code", "execution_count": 1, - "id": "83ddbd07-f7ab-4d80-b357-3890622d395f", + "id": "e5fc6812-3e72-4415-b4ca-5e873d81041b", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:19.815355Z", - "iopub.status.busy": "2024-05-07T15:48:19.814727Z", - "iopub.status.idle": "2024-05-07T15:48:20.366172Z", - "shell.execute_reply": "2024-05-07T15:48:20.365339Z" - }, "tags": [] }, "outputs": [], @@ -92,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "3496c5d6-7df6-49fb-b5b9-5ba48d2b7d62", + "id": "eba6ce0a-dd2b-4e34-8273-67cdd7706181", "metadata": {}, "source": [ "### Initialize the model with parameters" @@ -101,20 +95,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "bc9871c1-224e-4206-b39e-af7e448aca70", + "id": "2144f922-842a-4d1a-9c81-b87742fbbd73", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:20.372542Z", - "iopub.status.busy": "2024-05-07T15:48:20.370932Z", - "iopub.status.idle": "2024-05-07T15:48:21.344387Z", - "shell.execute_reply": "2024-05-07T15:48:21.343460Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -131,85 +119,58 @@ }, { "cell_type": "markdown", - "id": "abd17e36-857b-4101-9188-1be70206177e", + "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29", "metadata": { "tags": [] }, "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the QAOA algorithm [[1](#QAOA)], with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`." ] }, { "cell_type": "code", "execution_count": 3, - "id": "4233341d-139c-493b-b4af-210f71e3354a", + "id": "816b468f-a59f-4f2f-8337-4a9d66548425", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:21.352378Z", - "iopub.status.busy": "2024-05-07T15:48:21.350949Z", - "iopub.status.idle": "2024-05-07T15:48:23.150678Z", - "shell.execute_reply": "2024-05-07T15:48:23.150039Z" - }, "tags": [] }, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "qaoa_config = QAOAConfig(num_layers=20)" - ] - }, - { - "cell_type": "markdown", - "id": "e8e4f132-55d7-4a46-846c-b4d4b5edac57", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" + "combi = CombinatorialProblem(pyo_model=max_clique_model, num_layers=20)\n", + "\n", + "qmod = combi.get_model()" ] }, { "cell_type": "code", "execution_count": 4, - "id": "7384872d-a28f-49e3-918d-a5749c70d873", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:23.153812Z", - "iopub.status.busy": "2024-05-07T15:48:23.153151Z", - "iopub.status.idle": "2024-05-07T15:48:23.157294Z", - "shell.execute_reply": "2024-05-07T15:48:23.156687Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, + "id": "62ec28b3-cb49-411a-8c4a-8004fff6c105", + "metadata": {}, "outputs": [], "source": [ - "optimizer_config = OptimizerConfig(max_iteration=1, alpha_cvar=1)" + "write_qmod(qmod, \"max_clique\")" ] }, { "cell_type": "markdown", - "id": "01c3df80-5e78-4cf6-9429-4ef0eaafed78", + "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6", "metadata": {}, "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" + "## Synthesizing the QAOA Circuit and Solving the Problem\n", + "\n", + "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "3f848bc5-1ded-440a-b426-2f40e21949aa", + "execution_count": null, + "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:23.160101Z", - "iopub.status.busy": "2024-05-07T15:48:23.159651Z", - "iopub.status.idle": "2024-05-07T15:48:23.462012Z", - "shell.execute_reply": "2024-05-07T15:48:23.461349Z" - }, "pycharm": { "name": "#%%\n" }, @@ -217,16 +178,13 @@ }, "outputs": [], "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=max_clique_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" + "qprog = combi.get_qprog()\n", + "show(qprog)" ] }, { "cell_type": "markdown", - "id": "4e28c1f5-d1df-4600-a364-0ef54dab8209", + "id": "b119464b-9d46-4ea0-ba4a-1734f3e0e3e5", "metadata": {}, "source": [ "We also set the quantum backend we want to execute on:" @@ -235,139 +193,68 @@ { "cell_type": "code", "execution_count": 6, - "id": "794a44cd-6d2a-47b1-9e71-fb2d05a8442b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:23.465067Z", - "iopub.status.busy": "2024-05-07T15:48:23.464641Z", - "iopub.status.idle": "2024-05-07T15:48:23.482772Z", - "shell.execute_reply": "2024-05-07T15:48:23.482176Z" - }, - "tags": [] - }, + "id": "7d188c69-21d1-4afe-86b1-46229e91a01e", + "metadata": {}, "outputs": [], "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", + "from classiq.execution import *\n", "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n", ")" ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1acdcc28-3101-4a80-9b36-87cd5780abbb", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:23.485247Z", - "iopub.status.busy": "2024-05-07T15:48:23.484855Z", - "iopub.status.idle": "2024-05-07T15:48:23.511694Z", - "shell.execute_reply": "2024-05-07T15:48:23.511113Z" - } - }, - "outputs": [], - "source": [ - "write_qmod(qmod, \"max_clique\")" - ] - }, { "cell_type": "markdown", - "id": "6e3057bc-33bc-4a2c-818e-b967598c2141", + "id": "07621d7c-0e54-4adb-9c47-8fd99a346e29", "metadata": {}, "source": [ - "## Synthesizing the QAOA Circuit and Solving the Problem\n", - "\n", - "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" + "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[2](#cvar)]:" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "ad8e435d-4482-4d5f-ad53-886581d4fea0", + "execution_count": 7, + "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:23.514439Z", - "iopub.status.busy": "2024-05-07T15:48:23.513955Z", - "iopub.status.idle": "2024-05-07T15:48:49.929603Z", - "shell.execute_reply": "2024-05-07T15:48:49.928841Z" - }, - "pycharm": { - "name": "#%%\n" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/88ddbcbe-e798-4683-b7be-3052b1efec1a?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], + "outputs": [], "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" + "optimized_params = combi.optimize(execution_preferences, maxiter=1, quantile=1)" ] }, { "cell_type": "markdown", - "id": "1f78ae22-4ab8-4890-ad74-227dedcbde75", - "metadata": {}, - "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "27afef0a-52a4-4930-8a3a-f509bd5956bd", + "id": "615ed612-b835-4bf0-aa92-92d30ef8006d", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:49.934524Z", - "iopub.status.busy": "2024-05-07T15:48:49.933310Z", - "iopub.status.idle": "2024-05-07T15:48:56.641053Z", - "shell.execute_reply": "2024-05-07T15:48:56.640136Z" - }, "tags": [] }, - "outputs": [], "source": [ - "result = execute(qprog).result_value()" + "# Optimization Results" ] }, { "cell_type": "markdown", - "id": "f4f0b68a-e56f-4d11-b9f8-fe3b9bdab697", - "metadata": { - "tags": [] - }, + "id": "2510a439-9181-4e39-a033-0bd53e8f87f6", + "metadata": {}, "source": [ - "# Optimization Results" + "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the pyomo to qmod translator." ] }, { "cell_type": "markdown", - "id": "bdacb755-5cdc-45c2-95d5-037efe20f9d6", + "id": "e06ae35f-7aac-4631-9fe9-4f46a36c8cea", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm:" + "In order to get samples with the optimized parameters, we call the `get_results` method:" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "0134df1a-8894-4a1f-b6da-dc1883a49599", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:56.653630Z", - "iopub.status.busy": "2024-05-07T15:48:56.645232Z", - "iopub.status.idle": "2024-05-07T15:48:56.764591Z", - "shell.execute_reply": "2024-05-07T15:48:56.763847Z" - }, - "tags": [] - }, + "execution_count": 8, + "id": "9638f749-a60b-4176-a4ea-50d7c2bb986f", + "metadata": {}, "outputs": [ { "data": { @@ -390,105 +277,76 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", - " 115\n", - " 0.001\n", - " 4.0\n", - " [0, 1, 1, 1, 0, 1, 0]\n", - " 1\n", + " 99\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'...\n", + " 0.000977\n", + " -4.0\n", " \n", " \n", - " 90\n", - " 0.003\n", - " 4.0\n", - " [1, 1, 1, 1, 0, 0, 0]\n", - " 3\n", + " 102\n", + " {'x_0': 1, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'...\n", + " 0.000977\n", + " -4.0\n", " \n", " \n", - " 107\n", - " 0.001\n", - " 3.0\n", - " [0, 1, 0, 0, 0, 1, 1]\n", - " 1\n", + " 17\n", + " {'x_0': 1, 'x_1': 0, 'x_2': 0, 'x_3': 1, 'x_4'...\n", + " 0.015137\n", + " -3.0\n", " \n", " \n", - " 111\n", - " 0.001\n", - " 3.0\n", - " [1, 0, 0, 1, 1, 0, 0]\n", - " 1\n", + " 26\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", + " 0.012695\n", + " -3.0\n", " \n", " \n", - " 89\n", - " 0.003\n", - " 3.0\n", - " [0, 1, 1, 0, 0, 1, 0]\n", - " 3\n", + " 27\n", + " {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", + " 0.012695\n", + " -3.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "115 0.001 4.0 [0, 1, 1, 1, 0, 1, 0] 1\n", - "90 0.003 4.0 [1, 1, 1, 1, 0, 0, 0] 3\n", - "107 0.001 3.0 [0, 1, 0, 0, 0, 1, 1] 1\n", - "111 0.001 3.0 [1, 0, 0, 1, 1, 0, 0] 1\n", - "89 0.003 3.0 [0, 1, 1, 0, 0, 1, 0] 3" + " solution probability cost\n", + "99 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'... 0.000977 -4.0\n", + "102 {'x_0': 1, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'... 0.000977 -4.0\n", + "17 {'x_0': 1, 'x_1': 0, 'x_2': 0, 'x_3': 1, 'x_4'... 0.015137 -3.0\n", + "26 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.012695 -3.0\n", + "27 {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.012695 -3.0" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " max_clique_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)" - ] - }, - { - "cell_type": "markdown", - "id": "2724dafb-eb26-4756-8086-e415cecb0e78", - "metadata": {}, - "source": [ - "## Resulting Clique" + "optimization_result = combi.get_results()\n", + "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "4d99764d-8ff1-4397-9dac-c5e584862bfb", + "execution_count": 9, + "id": "9b868135-e219-441c-8d98-6b43d894a130", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:56.769629Z", - "iopub.status.busy": "2024-05-07T15:48:56.768261Z", - "iopub.status.idle": "2024-05-07T15:48:57.012646Z", - "shell.execute_reply": "2024-05-07T15:48:57.011984Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -498,8 +356,8 @@ } ], "source": [ - "solution = optimization_result.solution[optimization_result.cost.idxmax()]\n", - "solution_nodes = [v for v in graph.nodes if solution[v]]\n", + "solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", + "solution_nodes = [v for v in graph.nodes if solution[f\"x_{v}\"]]\n", "solution_edges = [\n", " (u, v) for u, v in graph.edges if u in solution_nodes and v in solution_nodes\n", "]\n", @@ -516,7 +374,7 @@ }, { "cell_type": "markdown", - "id": "d4d30ce4-f2b3-4049-b0e0-3cdc7b4897a1", + "id": "687f492b-a4a5-49c6-964c-8959b035bb93", "metadata": {}, "source": [ "And the histogram:" @@ -524,70 +382,87 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "c3233840-123c-4e29-8368-87ea3c135863", + "execution_count": 13, + "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:57.016876Z", - "iopub.status.busy": "2024-05-07T15:48:57.015879Z", - "iopub.status.idle": "2024-05-07T15:48:57.334652Z", - "shell.execute_reply": "2024-05-07T15:48:57.333914Z" - }, "tags": [] }, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "array([[]], dtype=object)" + "
" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + ")\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" + ] + }, + { + "cell_type": "markdown", + "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0", + "metadata": {}, + "source": [ + "Let us plot the solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", + "metadata": { + "tags": [] + }, + "outputs": [ { "data": { - "image/png": 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AAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsCimMrFq1SsnJyYqJiVFmZqa2bdvWo+3Wr1+viIgIzZgxI5TDAgCAfijoMLJhwwa53W4VFxdrx44dmjRpknJycnTs2LELbnfw4EH967/+q+64446QiwUAAP1P0GGkvLxc+fn5ysvLU0pKiiorK+VyuVRVVdXtNh0dHbrvvvtUWlqqMWPG9KpgAADQvwwKZnJ7e7u2b9+uwsJC/5jD4VB2drYaGhq63e6xxx7TyJEjdf/99+t3v/vdRY/j8Xjk8Xj8y62trZIkr9crr9cbTMn93vl+0Jfg0bveoX+ho3ehC4feOSON7RK65HSYgN9f1Ff97Ol+gwojJ06cUEdHh+Lj4wPG4+PjtW/fvi63eeONN/Sf//mfamxs7PFxysrKVFpa2ml88+bNcrlcwZQ8YNTW1touIWzRu96hf6Gjd6G7knu3NMN2BRe2ON3Xaay6urpPjtXW1tajeUGFkWCdPn1aP/zhD7V69WrFxcX1eLvCwkK53W7/cmtrq5KSkjRt2jTFxsb2Ralhy+v1qra2VlOnTlVUVJTtcsIKvesd+hc6ehe6cOjdhJJNtkvoktNhtDjdp0ffdsjjiwhYt6skp0+Oef7OxsUEFUbi4uIUGRmplpaWgPGWlhYlJCR0mv/+++/r4MGDuvvuu/1jPt9niWzQoEHav3+/brzxxk7bOZ1OOZ3OTuNRUVFX7IvPNnoTOnrXO/QvdPQudFdy7zwdERefZJHHF9Gpxr7qZU/3G9QDrNHR0UpLS1NdXZ1/zOfzqa6uTllZWZ3mjxs3Tu+++64aGxv9P/fcc4/uuusuNTY2KikpKZjDAwCAfijo2zRut1uzZ89Wenq6MjIyVFFRobNnzyovL0+SNGvWLCUmJqqsrEwxMTGaMGFCwPZDhw6VpE7jAABgYAo6jOTm5ur48eMqKipSc3OzUlNTVVNT43+otampSQ4HX+wKAAB6JqQHWAsKClRQUNDlui1btlxw27Vr14ZySAAA0E9xCQMAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVSGFkVWrVik5OVkxMTHKzMzUtm3bup373//930pPT9fQoUN11VVXKTU1Vb/4xS9CLhgAAPQvQYeRDRs2yO12q7i4WDt27NCkSZOUk5OjY8eOdTl/+PDh+slPfqKGhga98847ysvLU15enjZt2tTr4gEAQPgLOoyUl5crPz9feXl5SklJUWVlpVwul6qqqrqcf+edd+o73/mOxo8frxtvvFHz58/XxIkT9cYbb/S6eAAAEP4GBTO5vb1d27dvV2FhoX/M4XAoOztbDQ0NF93eGKPf/va32r9/v3760592O8/j8cjj8fiXW1tbJUler1derzeYkvu98/2gL8Gjd71D/0JH70IXDr1zRhrbJXTJ6TABv7+or/rZ0/1GGGN63LUjR44oMTFR9fX1ysrK8o8vXLhQr7/+urZu3drldqdOnVJiYqI8Ho8iIyP19NNP6x/+4R+6PU5JSYlKS0s7ja9bt04ul6un5QIAAIva2to0c+ZMnTp1SrGxsd3OC+rKSKiuvvpqNTY26syZM6qrq5Pb7daYMWN05513djm/sLBQbrfbv9za2qqkpCRNmzbtgiczEHm9XtXW1mrq1KmKioqyXU5YoXe9Q/9CR+9CFw69m1ByZT4T6XQYLU736dG3HfL4IgLW7SrJ6ZNjnr+zcTFBhZG4uDhFRkaqpaUlYLylpUUJCQndbudwODR27FhJUmpqqvbu3auysrJuw4jT6ZTT6ew0HhUVdcW++GyjN6Gjd71D/0JH70J3JffO0xFx8UkWeXwRnWrsq172dL9BPcAaHR2ttLQ01dXV+cd8Pp/q6uoCbttcjM/nC3gmBAAADFxB36Zxu92aPXu20tPTlZGRoYqKCp09e1Z5eXmSpFmzZikxMVFlZWWSpLKyMqWnp+vGG2+Ux+NRdXW1fvGLX+iZZ565tGcCAADCUtBhJDc3V8ePH1dRUZGam5uVmpqqmpoaxcfHS5KamprkcHx+weXs2bOaM2eOPvzwQw0ePFjjxo3T888/r9zc3Et3FgAAIGyF9ABrQUGBCgoKuly3ZcuWgOXHH39cjz/+eCiHAQAAAwB/mwYAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgVUhhZNWqVUpOTlZMTIwyMzO1bdu2bueuXr1ad9xxh4YNG6Zhw4YpOzv7gvMBAMDAEnQY2bBhg9xut4qLi7Vjxw5NmjRJOTk5OnbsWJfzt2zZoh/84Ad67bXX1NDQoKSkJE2bNk2HDx/udfEAACD8BR1GysvLlZ+fr7y8PKWkpKiyslIul0tVVVVdzv/lL3+pOXPmKDU1VePGjdN//Md/yOfzqa6urtfFAwCA8DcomMnt7e3avn27CgsL/WMOh0PZ2dlqaGjo0T7a2trk9Xo1fPjwbud4PB55PB7/cmtrqyTJ6/XK6/UGU3K/d74f9CV49K536F/o6F3owqF3zkhju4QuOR0m4PcX9VU/e7rfCGNMj7t25MgRJSYmqr6+XllZWf7xhQsX6vXXX9fWrVsvuo85c+Zo06ZN2r17t2JiYrqcU1JSotLS0k7j69atk8vl6mm5AADAora2Ns2cOVOnTp1SbGxst/OCujLSW0uWLNH69eu1ZcuWboOIJBUWFsrtdvuXW1tb/c+aXOhkBiKv16va2lpNnTpVUVFRtssJK/Sud+hf6Ohd6MKhdxNKNtkuoUtOh9HidJ8efdshjy8iYN2ukpw+Oeb5OxsXE1QYiYuLU2RkpFpaWgLGW1palJCQcMFtly9friVLlujVV1/VxIkTLzjX6XTK6XR2Go+KirpiX3y20ZvQ0bveoX+ho3ehu5J75+mIuPgkizy+iE419lUve7rfoB5gjY6OVlpaWsDDp+cfRv3ibZsvW7p0qRYvXqyamhqlp6cHc0gAANDPBX2bxu12a/bs2UpPT1dGRoYqKip09uxZ5eXlSZJmzZqlxMRElZWVSZJ++tOfqqioSOvWrVNycrKam5slSUOGDNGQIUMu4akAAIBwFHQYyc3N1fHjx1VUVKTm5malpqaqpqZG8fHxkqSmpiY5HJ9fcHnmmWfU3t6u733vewH7KS4uVklJSe+qBwAAYS+kB1gLCgpUUFDQ5botW7YELB88eDCUQwAAgAGCv00DAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwaZLsAAMDlkfzwRtslBO3gkum2S8BlwJURAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYFVIYWbVqlZKTkxUTE6PMzExt27at27m7d+/W3/7t3yo5OVkRERGqqKgItVYAANAPDQp2gw0bNsjtdquyslKZmZmqqKhQTk6O9u/fr5EjR3aa39bWpjFjxujee+/Vj3/840tSNABgYEh+eGPAsjPSaGmGNKFkkzwdEZaqwqUW9JWR8vJy5efnKy8vTykpKaqsrJTL5VJVVVWX87/2ta9p2bJl+v73vy+n09nrggEAQP8S1JWR9vZ2bd++XYWFhf4xh8Oh7OxsNTQ0XLKiPB6PPB6Pf7m1tVWS5PV65fV6L9lx+oPz/aAvwaN3vUP/Qmerd85Ic1mP1xecDhPwGz13od711Wuxp/sNKoycOHFCHR0dio+PDxiPj4/Xvn37gtnVBZWVlam0tLTT+ObNm+VyuS7ZcfqT2tpa2yWELXrXO/QvdJe7d0szLuvh+tTidJ/tEsJWV72rrq7uk2O1tbX1aF7Qz4xcDoWFhXK73f7l1tZWJSUladq0aYqNjbVY2ZXH6/WqtrZWU6dOVVRUlO1ywgq96x36FzpbvZtQsumyHauvOB1Gi9N9evRthzw+nhkJxoV6t6skp0+Oef7OxsUEFUbi4uIUGRmplpaWgPGWlhYlJCQEs6sLcjqdXT5fEhUVxT963aA3oaN3vUP/Qne5e9efHvj0+CL61flcTl31rq9ehz3db1APsEZHRystLU11dXX+MZ/Pp7q6OmVlZQVXIQAAgEK4TeN2uzV79mylp6crIyNDFRUVOnv2rPLy8iRJs2bNUmJiosrKyiR99tDrnj17/P99+PBhNTY2asiQIRo7duwlPBUAABCOgg4jubm5On78uIqKitTc3KzU1FTV1NT4H2ptamqSw/H5BZcjR47o1ltv9S8vX75cy5cv1ze+8Q1t2bKl92cAAADCWkgPsBYUFKigoKDLdV8OGMnJyTKGj2ABAICu8bdpAACAVYQRAABgFWEEAABYRRgBAABWXZHfwApgYPnyX2YNBweXTLddAtBvcGUEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVg2wXAFzJkh/eaLuEoB1cMt12CQAQFK6MAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsG/Dew8g2bAADYFdKVkVWrVik5OVkxMTHKzMzUtm3bLjj/hRde0Lhx4xQTE6NbbrlF1dXVIRULAAD6n6DDyIYNG+R2u1VcXKwdO3Zo0qRJysnJ0bFjx7qcX19frx/84Ae6//77tXPnTs2YMUMzZszQrl27el08AAAIf0GHkfLycuXn5ysvL08pKSmqrKyUy+VSVVVVl/P//d//Xd/85je1YMECjR8/XosXL9Ztt92mn/3sZ70uHgAAhL+gnhlpb2/X9u3bVVhY6B9zOBzKzs5WQ0NDl9s0NDTI7XYHjOXk5Ojll1/u9jgej0cej8e/fOrUKUnSxx9/LK/XG0zJFzXo07OXdH+Xw0cffeT/b6/Xq7a2Nn300UeKioqyWFX46Unvwv310Zcu5WtvoPXZ1vs2HPv8ZYN8Rm1tPg3yOtThi7BdTli5UO/66t+N06dPS5KMMReuLZidnjhxQh0dHYqPjw8Yj4+P1759+7rcprm5ucv5zc3N3R6nrKxMpaWlncZHjx4dTLn9VtxTtivAlYzXx+VBn+2ZabuAMNZd7/r69Xz69Gldc8013a6/Ij9NU1hYGHA1xefz6eOPP9a1116riAiS8Be1trYqKSlJf/7znxUbG2u7nLBC73qH/oWO3oWO3oXORu+MMTp9+rSuu+66C84LKozExcUpMjJSLS0tAeMtLS1KSEjocpuEhISg5kuS0+mU0+kMGBs6dGgwpQ44sbGxvDFDRO96h/6Fjt6Fjt6F7nL37kJXRM4L6gHW6OhopaWlqa6uzj/m8/lUV1enrKysLrfJysoKmC9JtbW13c4HAAADS9C3adxut2bPnq309HRlZGSooqJCZ8+eVV5eniRp1qxZSkxMVFlZmSRp/vz5+sY3vqGnnnpK06dP1/r16/X222/r2WefvbRnAgAAwlLQYSQ3N1fHjx9XUVGRmpublZqaqpqaGv9Dqk1NTXI4Pr/gMnnyZK1bt06PPPKI/u3f/k1f+cpX9PLLL2vChAmX7iwGMKfTqeLi4k63tXBx9K536F/o6F3o6F3oruTeRZiLfd4GAACgD/GH8gAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYSRMHHw4EHdf//9Gj16tAYPHqwbb7xRxcXFam9vD5gTERHR6eett94K2NcLL7ygcePGKSYmRrfccouqq6sv9+lcVj3pnSS98847uuOOOxQTE6OkpCQtXbq0074GWu8k6YknntDkyZPlcrm6/Sbkrl5369evD5izZcsW3XbbbXI6nRo7dqzWrl3b98Vb1pPeNTU1afr06XK5XBo5cqQWLFigTz/9NGDOQOxdV5KTkzu9zpYsWRIwpyfv44Fo1apVSk5OVkxMjDIzM7Vt2zbbJQUyCAu/+c1vzI9+9COzadMm8/7775tf//rXZuTIkeahhx7yz/nTn/5kJJlXX33VHD161P/T3t7un/Pmm2+ayMhIs3TpUrNnzx7zyCOPmKioKPPuu+/aOK3Loie9O3XqlImPjzf33Xef2bVrl/nVr35lBg8ebH7+85/75wzE3hljTFFRkSkvLzdut9tcc801Xc6RZNasWRPwuvvLX/7iX//BBx8Yl8tl3G632bNnj1m5cqWJjIw0NTU1l+ks7LhY7z799FMzYcIEk52dbXbu3Gmqq6tNXFycKSws9M8ZqL3ryg033GAee+yxgNfZmTNn/Ot78j4eiNavX2+io6NNVVWV2b17t8nPzzdDhw41LS0ttkvzI4yEsaVLl5rRo0f7l8+HkZ07d3a7zd/93d+Z6dOnB4xlZmaaf/qnf+qrMq9IX+7d008/bYYNG2Y8Ho9/bNGiReamm27yLw/03q1Zs+aCYeR//ud/ut124cKF5uabbw4Yy83NNTk5OZewwitXd72rrq42DofDNDc3+8eeeeYZExsb638tDvTefdENN9xgVqxY0e36nryPB6KMjAwzd+5c/3JHR4e57rrrTFlZmcWqAnGbJoydOnVKw4cP7zR+zz33aOTIkZoyZYpeeeWVgHUNDQ3Kzs4OGMvJyVFDQ0Of1nql+XLvGhoa9P/+3/9TdHS0fywnJ0f79+/XJ5984p9D77o3d+5cxcXFKSMjQ1VVVTJf+D5Fete1hoYG3XLLLf5vsJY+60tra6t2797tn0PvPrdkyRJde+21uvXWW7Vs2bKAW1o9eR8PNO3t7dq+fXvAa8jhcCg7O/uKeg0F/XXwuDIcOHBAK1eu1PLly/1jQ4YM0VNPPaXbb79dDodDL730kmbMmKGXX35Z99xzjySpubk54B8+SYqPj1dzc/Nlrd+mrnrX3Nys0aNHB8w736fm5mYNGzaM3l3AY489pr/+67+Wy+XS5s2bNWfOHJ05c0bz5s2T1P3rrrW1VX/5y180ePBgG2Vb111fzq+70JyB2Lt58+bptttu0/Dhw1VfX6/CwkIdPXpU5eXlknr2Ph5oTpw4oY6Oji5fQ/v27bNUVWdcGbHs4Ycf7vLhvy/+fPkFc/jwYX3zm9/Uvffeq/z8fP94XFyc3G63MjMz9bWvfU1LlizR3//932vZsmWX+7Qui0vZu4EmlN5dyKOPPqrbb79dt956qxYtWqSFCxfyukOPBNNPt9utO++8UxMnTtQ///M/66mnntLKlSvl8XgsnwV6iysjlj300EP60Y9+dME5Y8aM8f/3kSNHdNddd2ny5Mk9+svHmZmZqq2t9S8nJCSopaUlYE5LS4sSEhKCK/wKcCl7111fzq+70JyB0LtgZWZmavHixfJ4PHI6nd32LjY2Nuz+n/2l7F1CQkKnTzX09HUXjr3rSm/6mZmZqU8//VQHDx7UTTfd1KP38UATFxenyMjIK/7fLsKIZSNGjNCIESN6NPfw4cO66667lJaWpjVr1gT8deTuNDY2atSoUf7lrKws1dXV6V/+5V/8Y7W1tcrKygq6dtsuZe+ysrL0k5/8RF6vV1FRUZI+68tNN93kv7Q7UHsXisbGRg0bNsz/10GzsrI6fQya3n3WlyeeeELHjh3TyJEjJX3Wl9jYWKWkpPjn9JfedaU3/WxsbJTD4fD3rifv44EmOjpaaWlpqqur04wZMyRJPp9PdXV1KigosFvcF9l+ghY98+GHH5qxY8eav/mbvzEffvhhwEfbzlu7dq1Zt26d2bt3r9m7d6954oknjMPhMFVVVf45b775phk0aJBZvny52bt3rykuLu73H0/tSe9Onjxp4uPjzQ9/+EOza9cus379euNyuTp9tHeg9c4YYw4dOmR27txpSktLzZAhQ8zOnTvNzp07zenTp40xxrzyyitm9erV5t133zV//OMfzdNPP21cLpcpKiry7+P8x1MXLFhg9u7da1atWjUgPp56sd6d/2jvtGnTTGNjo6mpqTEjRozo8qO9A613X1ZfX29WrFhhGhsbzfvvv2+ef/55M2LECDNr1iz/nJ68jwei9evXG6fTadauXWv27NljHnjgATN06NCAT3HZRhgJE2vWrDGSuvw5b+3atWb8+PHG5XKZ2NhYk5GRYV544YVO+/qv//ov89WvftVER0ebm2++2WzcuPFynspl15PeGWPMH/7wBzNlyhTjdDpNYmKiWbJkSad9DbTeGWPM7Nmzu+zda6+9Zoz57HtcUlNTzZAhQ8xVV11lJk2aZCorK01HR0fAfl577TWTmppqoqOjzZgxY8yaNWsu/8lcZhfrnTHGHDx40HzrW98ygwcPNnFxceahhx4yXq83YD8DsXdftn37dpOZmWmuueYaExMTY8aPH2+efPJJc+7cuYB5PXkfD0QrV640f/VXf2Wio6NNRkaGeeutt2yXFCDCmC98/g4AAOAy49M0AADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArPr/wAHAChJa4zwAAAAASUVORK5CYII=", "text/plain": [ - "
" + "{'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4': 0, 'x_5': 1, 'x_6': 0}" ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", + "best_solution" ] }, { "cell_type": "markdown", - "id": "896bd3ad-ac54-4e21-b654-ba00743ece86", + "id": "149932e1-bfa8-4c27-b5f9-037e74eba400", "metadata": {}, "source": [ - "Lastly, we can compare to the classical solution of the problem:" + "## Comparison to a classical solver" ] }, { "cell_type": "markdown", - "id": "c9b54c5b-10e5-4f9c-9e00-44ac2cf24a33", + "id": "dde1905d-aeff-4297-a9d3-ad14910e6161", "metadata": {}, "source": [ - "## Classical optimizer results" + "Lastly, we can compare to the classical solution of the problem:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "dd5a1911-0eea-47fc-82f6-8942c8a4eac8", + "execution_count": 15, + "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:57.339572Z", - "iopub.status.busy": "2024-05-07T15:48:57.338409Z", - "iopub.status.idle": "2024-05-07T15:48:57.474823Z", - "shell.execute_reply": "2024-05-07T15:48:57.474130Z" - }, "pycharm": { "name": "#%%\n" }, @@ -598,28 +473,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model unknown\n", - "\n", - " Variables:\n", - " x : Size=7, Index=x_index\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : 1.0 : 1 : False : False : Binary\n", - " 1 : 0 : 1.0 : 1 : False : False : Binary\n", - " 2 : 0 : 1.0 : 1 : False : False : Binary\n", - " 3 : 0 : 1.0 : 1 : False : False : Binary\n", - " 4 : 0 : 0.0 : 1 : False : False : Binary\n", - " 5 : 0 : 3.9960192291414966e-08 : 1 : False : False : Binary\n", - " 6 : 0 : 0.0 : 1 : False : False : Binary\n", - "\n", - " Objectives:\n", - " value : Size=1, Index=None, Active=True\n", - " Key : Active : Value\n", - " None : True : 4.0000000399601925\n", - "\n", - " Constraints:\n", - " clique_constraint : Size=1\n", - " Key : Lower : Body : Upper\n", - " None : 0.0 : 7.992038458282993e-08 : 0.0\n" + "Classical solution: [1, 1, 1, 1, 0, 0, 0]\n" ] } ], @@ -628,27 +482,23 @@ "\n", "solver = SolverFactory(\"couenne\")\n", "solver.solve(max_clique_model)\n", - "\n", - "max_clique_model.display()" + "classical_solution = [\n", + " int(pyo.value(max_clique_model.x[i])) for i in range(len(max_clique_model.x))\n", + "]\n", + "print(\"Classical solution:\", classical_solution)" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "5fccfe7a-4567-4c2c-8287-1cae1b75e875", + "execution_count": 16, + "id": "79666d4d-e105-4706-b44e-e5c73b928af3", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:48:57.479695Z", - "iopub.status.busy": "2024-05-07T15:48:57.478528Z", - "iopub.status.idle": "2024-05-07T15:48:57.808411Z", - "shell.execute_reply": "2024-05-07T15:48:57.807685Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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bUAIvlxZy27MHGbt2oVl5ObR69EDiihWsYxFCFBSP45RwTIcQIlGVpaW46OwM9+RkRJubo1t8PBrq6bGOJVXHGjeGbkUF3CorWUeRusK0NGS4uKBbfj4iP/0UbidPKvwsfUKIbFEPJSHkox4lJyPVyAguycmIGDYMPW7fVvpiEnh5D6Uy91C+Tc/SEt1ycxHZqxfczp1DorExijIyWMcihCgQKigJIR907a+/IO7SBYYlJbj5559wP3BAae+XfBcHKOWknA/hq6vD49w5XF62DOb5+XhuZYUbe/awjkUIURCqcWYghNQKJxYjcsQIWH35JR41bgxeYiJsv/ySdSyZ4niq0j/5b/YLFqA0KgrFWlpo+/nniBwzhp6uQwipFhWUhJB/KX/yBNHt28PtwAHE2NqifU4ODDt3Zh1L5jgeT2UPkC179IBFTg7ibGzg9s8/iLayQml+PutYhBA5pqrHS0LIe2THxOCOiQns09IQPWkS3JOToamtzToWMzwVGvJ+VwMdHbhdv47oL79Et/R0PDA1RebZs6xjEULkFBWUhBAAwOU1a9CgRw/ovHiBe3v2oOfWrawjMSVW4R7Kt/X8809kHTmCBlVVaNanD+Lmz2cdiRAih2jZIEJUHCcWI8LHB65hYbiqp4c2sbHQs7RkHYu5w02bolVJCRyqqlhHkQvF9+/jhpMTnHJzEe7khJ7nz0NdS4t1LEKInKALcEJUWEleHmJbtYJHWBiinJzQOTeXislXVPkeyvdpamYGx6wshHt5oWdsLJKNjfE4JYV1LEKInKDjJSEq6t7p08ht3Rods7IQM2sWPGJioKapyTqW3FCldShrisfnwyMkBNfXr0fL4mKIOnXCtS1bWMcihMgBKigJUUHxP/wA3X79oC4W4+GxY3Bes4Z1JLlDBeWHdZ4+HVxiIh5pa6P9lCmIGDKElhYiRMVRQUmIChGLRAh3d4fDsmW42aIF9NLTYTFwIOtYckuVFjavLcPOndEhNxcXunWDe0AAYlq3RkleHutYhBBGqKAkREUUZ2Yi0dgYbpGRCO/TB90fPEBTMzPWseQX9VBWS6NRI3hcuoSYb79FxwcP8LBVK2SEhLCORQhhgApKQlTAbaEQTywsYJmfj8QlS+Bx+jT46uqsY8k1MRWUNeb866/IDwuDmMeDoY8PLk6fzjoSIUTGqKAkRMldnDYNJkOHolxdHcVnzqD7okWsIykMKihrru1nn8H4/n0ktWoFl40bEdGlCypKSljHIoTICBWUhCgpUXk5wu3t4fLbb7jaqhVaZmai1aefso6lMGjZoNpr3Lw5XO7cQYS/P5yvXsVNY2PkXrrEOhYhRAboeEmIEnqckoJrRkbomZiIiMGD4XLnDho3b846lmKhIe864fH5cD90CLe3boVBaSnUHRxw5ddfWccihEgZFZSEKJnUXbtQaWeHlsXFuL5+PdwDAsDj0z/12uJ4PJV+lnd9dZw0CZrXruG+ri46zZqF8AEDIBaJWMcihEgJnWUIUSJR48bBfNw4FGppQRQXh840OaLOaMi7/gysrdE5NxdRPXvC4+RJJJiaojgzk3UsQogU0PGSECXw4ulTRHboANdduxDXvj0sc3Jg1L0761iKjYa8JUJNUxMeUVFI+PFHWD18iCcWFrh18CDrWIQQCaOCkhAFl5uQgDRjYzjeuIGoMWPgduMGGujosI6l8OhJOZLVffFiPAsPR6mGBsyGD0f0pEmsIxFCJIgKSkIU2NUNG6Du6Ai98nKk79gB1127WEdSKlRQSpapmxva5OQgwcoKPbdvR6S1NcqfPGEdixAiAVRQEqKAOLEYEX5+6DhjBrKaNoVGUhJsxo9nHUupcHw+HSClQKtZM7jdvImocePQ/eZN3DU2xoPISNaxCCH1RMdLQhRMaX4+Lpqbw/3oUUR36wbb3Fx8YmPDOpZSoh5K6XHduROZ+/ahUWUltD08kLBkCetIhJB6oIKSEAVyPzwcD0xN0fnePVycOhUely5BXUuLdSylRD2U0td+xAg0vX0bt5s3R/fFixHu5oaqigrWsQghdUDHS0IUxKVly9Dk00+hJRIh+/BhuGzcyDqS0qMeSulr1qYNumdlIbxfP7hGReGqkREKbt1iHYsQUktUUBIi58QiEcL79kXXH35AuoEBmt6+jXZDh7KOpfz4fCooZYSvrg6PkyeRtHo1zIqKUG5jg+vbt7OORQipBSooCZFjT7OykGBqCo8zZxDp5oZuOTlo1qYN61gqgRY2l72u338PUVwcChs2RLtJkxAxfDg4sZh1LEJIDdDxkhA5lRESgnxzc7TPy0P8woXwiIgAX12ddSyVQj2UsmfUvTuscnMRY2cH94MHcdHcHM8fPWIdixBSDSooCZFDsbNno7mPD8Q8HgpPnIDD0qWsI6kempTDjKa2NtyvXsXFqVNhd+8ecszMcOf4cdaxCCEfQcdLQuRIVUUFwp2d4bRmDa63bIkW9+6hTf/+rGOpJnpSDnMuGzci79gx8DkOBp6eiPn+e9aRCCEfQAUlIXKiMC0NV42M4Bobi3BPTzhlZkK7RQvWsVQW3UMpHywGDkTzu3dx3dQUzr/8gnB7e1SWlrKORQh5Bx0vCZEDN/fvR6m1NVoXFSFp9Wp4hIaCx6d/nkxRD6XcaGJsDOd79xAxeDB6JCYi1cgID69eZR2LEPIWOmMRwlj0lCloNXIknmlqoiwqCl1pWE8+0LJBcoXH58M9IAA3Nm1C85IS8Lp1w9UNG1jHIoS8QgUlIYxUlJQgolMn9NyyBZcsLNA6Kwste/RgHYu8QkPe8qnTV19BPSkJ2To66DhjBsK9vWlpIULkAB0vCWHg4dWruGFiAudr1xA5fDh63rqFhnp6rGORd1APpXz6pGNH2ObmItrJCR6hoYgzNUXx/fusYxGi0qigJETGrm3ZAq5bNxiWlODWn3/Cbf9+ul9SHqmp0QFSjqlracEjJgZx8+bBOicHhRYWuC0Uso5FiMqi4yUhMsKJxYgcMQJWU6bgUePG4CUmwvbLL1nHIh9Ck3IUguPy5Xhy5gwq1NTQcuhQXPjqK9aRCFFJVFASIgNlhYWItrKC24EDiLG1RfucHBh27sw6FvkYuodSYbTq3RumDx7gctu26PHnn4js2BEvnj5lHYsQlULHS0KkLOvCBdw1NYV9ejqiv/gC7snJ0NTWZh2LVId6KBVKIwMD9EhLQ+SoUXBMSUG6sTGyY2JYxyJEZVBBSYgUXV69Gg1dXdGkogKZ+/ah519/sY5EaoqWDVI4PD4fbnv2IGPXLjQtL4dWjx649PPPrGMRohKooCRECjixGOFeXrCbMwd39fTQODUV7UeMYB2L1AYNeSusDmPGoNGNG7ijr4+uCxci/NNPIRaJWMciRKnR8ZIQCSvJy0Nsq1bwCAtDlJMTuuTkQM/SknUsUls05K3Q9Cwt0S03F5G9esHt/HkkGhujMC2NdSxClBYVlIRI0N2TJ5HXujU6ZmUhZtYseMTEQE1Tk3UsUhd8Ph0gFRxfXR0e587h8rJlMM/PR6m1NVJ372YdixClRMdLQiQkbsEC6A0YAD7H4VFwMJzXrGEdidQH9VAqDfsFC1AaFYViLS2Yjx2LyNGj6ek6hEgYFZSE1JNYJEK4uzscly/HzRYtYJCRAXNvb9axSD3x1NSgxjoEkZiWPXrAIicHcTY2cNu7FxfatUNpfj7rWIQoDSooCamHJ3fvItHYGG6RkQjv0wfdHzyATsuWrGMRSeC97J+knizl0UBHB27XryP6yy/RNSMDD0xNkXn2LOtYhCgFKigJqaPbQiGK27WDRX4+Li9dCo/Tp8FXV2cdi0jKq8dhiquqGAchktbzzz+RdeQINKuq0KxPH8TNn886EiEKjwpKQurg4rRpMBk6FOXq6nh27hzsFy5kHYlI2qseyipabkYptRsyBHrp6bhhZATHFSsQ7uQEUXk561iEKCwqKAmpBVF5OcLt7eHy22+42qoVTB88gJmHB+tYRBpeFZTUQ6m8mpqZwTErC+FeXugZF4drRkZ4fP0661iEKCQqKAmpoccpKbhmZISeiYmI8PODy507aGRgwDoWkZZXQ96iigrGQYg08fh8eISE4Pr69TB5+hRVdnZI3ryZdSxCFA4VlITUQMrOnai0s0PL4mKkbNwId6EQPD7981FmPLqHUqV0nj4dXGIiHmprw/rrrxExeDBNyCKkFuiMSEg1osaNg8WECSjU0oIoLg52U6eyjkRkgXooVY5h587okJuLC/b2cA8MRGyrVniWk8M6FiEKgQpKQj7gxdOniLS2huuuXYiztoZlTg6MundnHYvICt1DqZI0GjWCR0ICYmbNgk1WFh63aYP0oCDWsQiRe1RQEvIeuQkJSDM2huPNm4gaOxZuqalooKPDOhaRIZ7ay2XNqyorGSchLDivWYOCEydQxeOhha8vLtLIBCEfRQUlIe+4umED1B0doVdejoy//4br33+zjkRYoB5Kldemf38Y37+PpNat4fL774iws0NFSQnrWITIJSooCXmFE4sR4eeHjjNmIKtpU2gkJaHD2LGsYxFGaFIOAYDGzZvDJSMDkcOHwzk5GbeMjJCbkMA6FiFyhwpKQgCU5ufjYtu2cD96FNHdusE2Nxef2NiwjkVYelVQ0pA34fH5cNu/H7e3bYNeWRk0HB1xec0a1rEIkStUUBKVdz88HA9MTdE5MxMXp06Fx6VLUNfSYh2LMPa6h5KWjiGvdZw4EVopKcjU1YXd7NkI79cPYnqSEiEAqKAkKi7hp5+g8+mn0BKJkH3kCFw2bmQdicgLWjaIvIe+lRU65+YiytUVHqdP45KJCZ7cvcs6FiHMUUFJVJJYJEJ4nz7o9uOPuP3JJ2iWno52Q4awjkXkCPVQkg9R09SER2QkEn78EZaPH+Npu3a4uX8/61iEMEUFJVE5xffvI8HUFB5nzyLS3R322dlo2qoV61hE3lAPJalG98WLURIejucaGmg1ciSiJkxgHYkQZqigJColPSgIhRYWaJ+Xh/iFC+ERHg6+ujrrWEQOUQ8lqQlTNze0yclBQvv2cN25E5Ht26P8yRPWsQiROSooicqI+f57GPr6QsTno+jUKTgsXco6EpFjb5YNokkXpBpazZrB7cYNRI0fj+63buGusTEeREayjkWITFFBSZReVUUFwp2c4PzLL7hmagqje/fQum9f1rGIvKN1KEktue7YgfsHDqBRZSWaeHggYfFi1pEIkRkqKIlSK0xLw1UjI7jGxSHcywvO9+5Bu0UL1rGIAqCFzUldWA0bhmbp6bjVvDm6L1mCcFdXVNF9uEQFUEFJlNaNvXtRam2N1kVFSFq9Gh4hIW+KBEKq8/pZ3jTkTWqraatW6J6VhfD+/eEaHY2rRkbIv3GDdSxCpIrOrkQpRU+ejNajR+NpgwYoi4pC1++/Zx2JKBjqoST1wVdXh8eJE0j+5ReYFRWhwtYW17dtYx2LEKmhgpIolYqSEkR06oSeW7fikqUl2mZno2WPHqxjEQVEPZREErp89x1E8fEoaNgQ7b74AhECAa0cQJQSFZREaTy8ehU3jY3hfO0aIkeMQM+bN6HVrBnrWERR8XgAaNkgUn9G9vawys1FTOfOcD98GBfbtsXzR49YxyJEoqigJErh2pYt4Lp1Q/Pnz3Fryxa47dtH90uSenm9PmlVZSXjJEQZaGprw/3KFVycNg12mZnIMTPDnePHWcciRGLojEsUGicWI2L4cFhNmYKH2trgX7kC28mTWcciSoAWNifS4LJhAx4GB4PPcfjE0xMx333HOhIhEkEFJVFYZYWFiLaygvvBg4jp1AnW2dlo3qkT61hEWdCQN5ESc29vNL97F9dMTeG8di3C7e1RWVrKOhYh9UIFJVFIWRcu4F7LlrBPT0f0l1/CPSkJmtrarGMRJfJ6Ug4NeRNpaGJsDOd79xDh54ceiYlINTJC3uXLrGMRUmdUUBKFc3n1ajR0dYV2ZSUy9+1Dzz//ZB2JKCEa8ibSxuPz4S4U4uaff6J5SQn43bvj6oYNrGMRUidUUBKFwYnFCPf0hN2cObirp4fGqaloP2IE61hESdGkHCIrtl9+CfXkZGTr6KDjjBkI9/KiCxmicKigJArhWU4OYs3M4HH8OKKcndElJwd6lpasYxFl9uoeSnAc2xxEJXxiYwPb3FxEOzvDIywMcS1bovj+fdaxCKkxKiiJ3Lt78iQetm2LjtnZiP3+e3hcvAg1TU3WsYiSox5KImvqWlrwuHgRcfPnwzo3F0UWFrh95AjrWITUCBWURK7FLVgAvQEDwOc4PAoOhtPq1awjERXxZh1T6qEkMub48894cvYsXqipoaW/P6KnTGEdiZBqUUFJ5JJYJEK4mxscly/HDSMjGGRkwNzbm3UsokLoWd6EpVaffgqz7GwkWlig55YtiLSxwYunT1nHIuSDqKAkcufJ3btINDaGW1QUwvv2hcP9+9Bp2ZJ1LKJiXg95c1RQEkYa6umh561biBozBg6pqcgwMkLWhQusYxHyXlRQErly+8gRFLdrB4v8fFxetgwep069ObETIlOvJuVQDyVhicfnw3XXLtz95x80qahAI1dXXFq2rNb7eV7wBBmnonDr2GlknIrC84Inkg9LVBoVlERuXJw6FSb+/ihXV8ezc+dgv2AB60hEhalpaACgHkoiH6xHj4b2zZvIMDBA1x9+QHivXhCLRB/dJjMyAXHeo5BtYIKGBrow7+8Gq0H9YN7fDQ0NdJFtYII471HIjEyQ0W9BlBl1/RCJeF7wBHmJ1yAqK4d6Qy206GaLxvrNarRtZWkpLri6wuPyZVxo3RpdEhLQyMBAuoEJqQ49epHIGV1zc3TLyUFk//5wO3cOiUZGML948T9LqOVcSUXB6PGwTY2HCY8Pde6/f4f5AEwKcmAYdgDqoftwrYMD9PfshHGXDjL6bYiyoR5KUmeSuPp9nJKCFGNj9Lh8GRFDhsAlI4OKSSIXXt9qUV0vECGyxFdXh8fZs7iyfDnaFhSg1Noaqbt2vXk/fsEq6Dl0gfWNSwDw3mLyba/ft75xCXoOXRC/YJX0whOlRgUlqbWcK6m4ZuOIVu4O6BZ2ACYFOf/5i/T66rdb2AG0cnfANRtH5FxJ/ddnUnbuRKWdHUyePkXqxo1wP3Lk/5dqIYQxevQikWfd5s1D+YULeKKlBfNx4xA5ahRiJsyEw/K5aCCqqLaQfJc6J0YDUQUcls9FzMRvpZSaKDM6e5NakdTVb9TYsbCYMAEFDRtCFB8Pu6lTpRuckFpSez0ZjApKIqdMnJ1hmZOD2I4doRVxFc471wMAeHXc3+vtnHesQ/xCWvOX1A4VlKTGYiZ+K5Gr3+PWTnDdvRtxHTqgXXY2jOztpZSYkLrjqakBoFneRL410NGB5e6D6PQwHR9agj8PgDsAA7w86fMATPrIPjkAnVb98J9RJUI+hgpKUiPxC1bBecc6APW/+v3sZhyCPQbCLSUFDXR0JJKPEEmjIW+iKApGj4d6leiDx+Z0AJEAngBoWoP98QCoV4lQMHq8hBISVUAFJalWzpVUdFq96L1Xv8cAtASggZcHIT5eHrB++Mj+OAB9o0/Q1S+Ra7SwOVEEmZEJsE2N/+ioUScASQBEADbUcL/qnBi2qfHIjL4kgZREFVBBSar1savfqwDKAfQE8DmAQa9+vgzA6A/sj65+iSLgvxryph5KIs/yVq+HiPfxU7kOXhaVtSXi8ZG3cl2dchHVQwUl+ajqrn5/BJAP4DyA3QACADwGoAXg8Ef2S1e/RN5RDyVRBC1jw2t9T3tNqXNimMRGSGXfRPlQQUk+qiZXv+/SxMsr4upW76OrXyLP6B5KIu9K8otgVJAj1TaMC7LpMY2kRuhJOeSjanr1+whAEYBsABtffd+qmm3o6pfIszdD3irUQ8mJxeDEYohFompfc1VV///6rffAcR/+XDWvwXH/+V4sEr335x98/epz73uNV+293ubNZ169B44Dx3HAu9tw3Mv339r+3Z+/u+3rP29//vXn3nxf3etq3nuGhvCR8t8JPoC8xGsw7+cq5ZaIoqOCknxQba5+PQDceOt7E7wcBq/O66vfmj6mURred1Ks6Yn0QyfRt7+v7vV7T5bVnFSrPXG+54T70ZPqx06ib33u7fc/dFL9z0m0pifPmrwGwHv3PQDguH//nOPAe9/rt7d//Rp48/7bX3llZQCA9nv2IOHYsf9+/tXXGr1+92fv/BwA+B94j/fuz97e5n3vvf75u5/5yOv3vUdqRvzqD/fqz4decwA4Hu+/P3/1s/d+/57XACB+9VjQHL2WsvgVISorl0k7RLFRQUk+6OHl6zCv4Wd/AXAdL5enOIaXB8znNdiODyDJ2g4tivP+e4L92Gv89yTKw79PyjU5kb79GgDUavj7qrqanEBfvwZengw/dnJ9+zPve8295+uH3nv352++r+nrV9+//jshBlD1epHztz7zr68AOD7/zbbg8V5+/57X4PP/v523/vxr+9evX33+X5/90Hsfeq2mBt4HPsN763uemlrNX7/els9/s+/X7/H4/P9s87qd2rx+3/fvtvH2z6t7DR4PfHX19773+ufvfqa618DLYwizAvxUFNDfTerNqDfUknobRPFRQUk+qDZXpZ6v/gDAXwD0ATgBeIrqD7b5pq1QYfZJ/U6a7550X594qju5vj4hvj55vfrZ2z9/70n39UnprZPe29tXe9J8z8nx3RPl65NbtT+v4Qn17W2qO7FWe0Kty18oBXM1JATw8cENgQAj33pWMiHyokU3W4gh3YJW/KodQqpDBSX5oPpclX4GYC+Ak69ef4zNip/p/hwid948V54m5RA51Vi/GbL1jWEixYk5OfomaMnwliSiOFSho4HU0eur37ooffU1r5rP0dUvkVdqGhoAoFKTcojiyXLyqNFKHP4A+gB4/YTu46++7wPg/ge2EfH4yHZyl0RMogKooCQf1Fi/GXL1jT/6mZT3/KwUwIlXr6vrnczRN2E6IYeQD6GFzYkiaDF7Ro1W4jgK4Cz+/5id8+r7s/hwQanOidFi7kxJxCQqgApK8lHVXf32BaAHoBeAsXh5tasLoAzAQAAtPrJvuvol8uzNOpTUQ0nkVFFGBjKnjMU1Q/NqeylFeGdS3Ft/er7v8zw+rnVwQKue9hJOTZQVFZTko6q7+h2Kl7NhI/HySTnnAGgDmI+Xs70/hq5+iTx7PeT9eokhQuSFWCRC1LhxEFtawu7GDTywt4NITR2S+pvKARCpqUN/z04J7ZGoAiooyUe1cuuOax0cPnj1uxFAAYAq/P9SMQUAfq5mvyIeH0mG5sjcvA5VFRWSjEyIRLwe8qZJOUSe3Dp4ECm6unDdtQs3WrfGi6QkeIYIkTz7pzdLXdUXD0DynKUw7tJBQnskqoAKSlIt/T07pXL1m9usMdz27cPtZs1wbcsWCe2dEMl4MymHCkoiB4ozMxFhZweL4cOhVVmJpI0b0fPOHTTv1AkA4PDzHPzd4+XibXU9Vr/eLnbit3BYNrv+oYlKoYKSVMu4SwepXP0OuJmEa1u2gOPxYDtlCqLNzfEoOVlCrRBSP296KGnImzDEicWInjIFL9q2RdfkZER5e6N1YSHspk791+f++OMPjL8Qhj1DJuCFumaNZn6/TcTj44W6JuIXrILTtl8l+SsQFUEFJakRh5/nIGbCy/sd63v1e27kV2+ufm0nT4ZVUREiP/8c1nfvQsvODuE+PqgoKal/aELqgf/q6Tg0KYewknb0KJL09NBzyxakm5igJCEBHsHB0GjU6F+fO3HiBKZNm4YZM2Zg9JHtKIy/ghvWLyfTVDtZ59X7N6ztURh/hXomSZ1RQUlqzHn7WsTPX1mvq9851o745ko4ioqK3rynpqkJt927oZaRgSu2tnANCcEDfX0krlwp6V+BkBqjHkrCyrOcHITb26ONnx90yspwedUquNy/DyP7/864TklJwbBhw/DZZ5/hl19+AfByVMk2JQ6ZEfFI9ByOLH2T/6wpLAbwQM8I21ta48Rfu2GbEkf3TJJ64XEcHS1J7eRcSUXB6PGwTY2HiMf/6Czw1+9f6+AA/T07UdJYHc7OzrCzs8OJEyegqan5n21uHTqE8i++gN3Tp4g1MkLLQ4fQsuf7FrYgRHry0tLQol07/N2/P8adOFH9BoTUEycWI2bmTLT5/Xc0FYsR368fnA8fRgMdnfd+/tGjR3B0dISOjg6io6PRpEmTD+77ecET5CVeg6isHOoNtdCimy0a6uqgZcuWEAgEWL9+vZR+K6IqqIeS1Jpxlw5odzkSTkZtEdqt1wevfrP0TZDoORyZUQlvrn7btWuHwMBAXLhwAV988QXedz1jJRCgU1ERLn7zDVo9fAh9V1eE9+qFssJCmfx+hAD/P+RNPZREFu6EheHyJ5/AZeNGZBoaojA6Gh4nT36wmCwvL8egQYNQVlaG4ODgjxaTwMsHVZj3c4WVb1+Y93NFY/1m4PP58Pf3x+HDhyGmyWeknqigJHVy7tw5xOXeQdsd69AyPwtl+UXIOBmJW4GnkHEyEmX5RWiZnwXHkL3/WRjX1dUVO3fuxO7du7F06dL37p/H58Plt9/QJDsbcU5OcAkPR76hIWLnzKFZt0QmaNkgIgvPHz1CuLMzWnp5weDpUyQsWQKnnBy07NHjg9twHIcJEybgypUrCAoKgpmZWZ3bFwgEyMnJwcWLF+u8D0IAABwhdTBx4kTO0tKSE4vFdd7HsmXLOADc7t27q/3s3VOnuPhPPuE4gLukp8dlhIbWuV1CaqIoN5fjAG7Hp5+yjkKUkLiqiouZPZt7oKbGlQHceQ8PrqyoqEbbLl68mAPAHTp0qN45qqqqOBMTE27q1Kn13hdRbdRDSWpNJBIhMDAQQ4YMAY9X98WE5s+fjwkTJmDixIkIDw//6Gdb9+0L+7w8xC9cCIOnT2Hq5YVwe3s8zcqqc/uEfAxNyiHSknnuHC61aAGn1auRq6eHh2fPwuP8eWg1a1bttvv378fixYuxbNky+Pv71zvL62HvI0eOoIpWNCD1QAUlqbXIyEgUFBTAz8+vXvvh8Xj4888/4e7ujsGDB+PGjRsf/zyfD4elS2H4+DEu9OkDh8RElLVqhejJkyEWieqVhZB3qb+aMEbLBhFJKSssRLi7Owx794ZRYSHi5s2DfV4eWn36aY22v3jxIsaPH48xY8Zg/vz5EsslEAiQm5uLCxcuSGyfRPVQQUlqTSgUwszMDPbvWcKitjQ0NHDkyBGYmJjA09MTDx8+rHYbrWbN4HH6NJ7ExiLDxAQ9t25Fiq4ubuzdW+88hLxGPZREkhIWL8YjQ0O4REYi1sUFenl5cFy+HDx+zU7D9+7dw6BBg+Dg4IC//vqrXqND73J0dISpqSkOHToksX0S1UMFJakVsViMo0ePws/PT2IHtKZNmyIsLAzl5eUYOHAgSktLa7SdsaMjXO7fx5Vff4VWZSWsRo9GZIcOKExLk0guotpe91DSpBxSH1nR0YgzMkL3JUuQr6OD7BMn4HHhAhoZGNR4H8XFxfD29kaTJk0QEBCABg0aSDQjn8/H0KFDIRQKadib1BkVlKRWYmNjkZubiyFDhkh0v2ZmZggJCcH169cxevToWh3Uunz7Ldo8eYIoPz/Y3bgBnpUVIoYPR1VFhUQzEtVCPZSkPl48fYrwvn2h5+oKs0ePEDNzJro+fow2/fvXaj8ikQjDhg1DVlYWQkNDYVCLQrQ2BAIB8vLyEB0dLZX9E+VHBSWpFaFQiBYtWsDFxUXi++7WrRsOHDiAY8eOYfbs2j3+S11LC+5CISquX8d1Cwu4HzyI9KZNkfT77xLPSVQDLRtE6ipx5UrkGBigx5kziLe3h/aDB3Beu7bGw9tvmzlzJs6cOYMjR46gffv2Ukj7kqOjI8zMzGjYm9QZFZSkxjiOg1AoxODBg8Gvw4GxJnx8fLBhwwasXbsWf/zxR623/8TGBq63byNlxw6I1NRgN3UqLrRujbzLl6WQlig7MUDrnpIay01IQIypKbrNm4fiRo2QGRgIj4QENDE2rtP+fv/9d/z+++/YtGkT+vTpI+G0/8bj8Wi2N6kXKihJjV2+fBmZmZn1nt1dnW+++QYzZszAtGnTEBISUqd92IwfD+snTxA1bhza3b+Pxt26IdzTExUlJRJOS5QZB9CQN6lWZWkpwr280MTBAeY5Objw1VewKyyEha9vnfd5/PhxTJ8+HTNnzsTkyZMlmPbDBAIBHj16hMjISJm0R5QLFZSkxoRCIfT09ODu7i71tn755RcMHDgQw4cPx+U69i7y1dXhunMnNO/exWU7O/Q8fhzZenq4tGyZhNMSZSUGaMibfNTV9euRqacH17AwJNrZocHdu+ixaVOdhrdfu379OoYNGwYvLy+sWbNGgmk/rnv37mjVqhUNe5M6oYKS1Mjr4W5fX19oaGhIvT01NTXs3bsXHTp0gLe3Nx48eFDnfTVt1QruV6/ibkAAnjRuDPsffkCckRHuV7OYOiHUQ0k+5FFyMi60aYPOM2eiTEMD6QcOwP3qVTStx2MQAeDhw4fw9vZG27ZtsW/fPqi9vpdXBng8HgQCAYRCIUS0ti+pJSooSY2kpqbi9u3bEp/d/TGNGjVCcHAwGjRoAE9PTxQXF9drf5aDB6NzQQEuzpgB08eP0bxXL4S7uqI0P19CiYmyoR5K8i5ReTki/PzQwM4OVpmZiBo/HjZFRbAaNqze+y4rK8OgQYPw4sULBAcHQ1tbWwKJa0cgEODx48eIiIiQedtEsVFBSWpEKBSiSZMmUr8x/F2GhoYICwtDVlYW/P39UVlZWa/98fh8uKxbh6Y5OYjt0QPO0dEoatECMd99R5MvyH9QDyV527UtW5ChqwvXo0eR1KED1NLS4LpjB/jq6vXeN8dxmDBhApKSkhAUFARTU1MJJK69bt26oU2bNjTsTWqNCkpSI0KhEN7e3hJfULcmrK2tERAQgPDwcHz99dfgJHCCb9y8OTyio5F39iyyDAzgvHYtrhgYIP3YMQkkJsqCA6iHkuBxSgqi2rWD7ZQpqOLzcfPvv+GWkgJdc3OJtbF48WIcOHAAu3fvRvfu3SW239qiYW9SV1RQkmqlp6cjOTlZpsPd7+rVqxe2bduGbdu2YeXKlRLbb6tPP4VjXh4SFi+GXkkJWg8ahIiuXVF8/77E2iCKSwxQD6UKq6qoQOSIEdCwtUXH9HREjhwJq6IidBg7VqLt7N27Fz/99BOWL1+OoUOHSnTfdSEQCFBQUIDz58+zjkIUCBWUpFoBAQFo2LAhBgwYwDTHmDFj8OOPP2L+/Pk4cOCARPfd/ccfYZSfj+j+/dHtyhVUtG6NqAkTIKYrdJVGQ96qK3XXLtzS1YXbgQO4ZmGBqpQUuO3dC7XXj+SUkIsXL2LChAkYO3Ys5s6dK9F911WXLl1gbm5Ow96kVqigJNUSCoX47LPP0LhxY9ZR8OOPP+Lzzz/H2LFjJf6IsAY6OvA4cQLPEhJw28wMrjt3IrVZM6Tu2iXRdojioEk5qqcwLQ2R1tZoP24c+ByHa1u2wPX2bRhYW0u8rbt372LQoEFwdHTEli1bwOPxJN5GXbwe9g4ICKj3fetEdVBBST7qwYMHiI+PZzrc/TYej4dt27bBxcUFvr6+SEtLk3gbRvb26HHvHpI2boRGVRXajxuHKCsr5N+4IfG2iHyjHkrVIRaJEDVuHDgrK9jdvImoIUNgUVgIWyktKl5cXAxvb2/o6OggICCAyf3pHyMQCFBYWIhz586xjkIUBBWU5KMCAgKgoaEBLy8v1lHe0NTUREBAAJo3bw5PT0/kS2nZH7upU2FeVIQof3/YpqVB3cYGEUOHQlReLpX2iPwRA+BRQan0bu7fjxRdXbju2oUbrVvjRVIS3I8cgbqWllTaE4lEEAgEyMnJQUhICAwMDKTSTn3Y2dnB0tKShr1JjVFBST5KKBSib9++aNq0Keso/6Krq4uwsDA8ffoUvr6+KJdSkaeupQX3Q4cgSklBspUVXIVC3NHVxdUNG6TSHpEv1EOp3IozMxHRqRMsR46EVmUlkjZuRM87d9C8UyeptclxHKZPn45z587hyJEjaN++vdTaqo/Xw95Hjx5FRUUF6zhEAVBBST4oLy8P0dHRcjPc/a42bdogODgYV65cwdixYyGW4r1uBtbWcLtxAzd37cILdXV0njEDF83MkJuQILU2CXu0bJBy4sRiRE+Zghdt26LrtWuI8vZG68JC2E2dKvW2f/vtN2zatAmbNm1C7969pd5efQgEAhQVFeHs2bOsoxAFQAUl+aBjx46Bz+fD19eXdZQPcnBwwN69e3H48GHMnz9f6u11GDMGNkVFiJ40CRbZ2dBxcEB4//548fSp1NsmskelpPJJO3oUybq66LllC9JNTFCSkACP4GBoNGok9bbDwsIwc+ZMfPfdd/jiiy+k3l592drawsrKioa9SY1QQUk+SCgUwsPDA/r6+qyjfNTgwYPx66+/YtWqVfjrr7+k3h5fXR09t25Fg7t3kdC1K3qcOoVcAwMkLF4s9baJbHEAeNRDqRSe5eQg3N4ebfz80KS8HJdXrYLL/fswsreXSfvXrl3D8OHD4e3tjVWrVsmkzfqiYW9SG1RQkvcqLCzE+fPn4efnxzpKjcyYMQP/+9//8PXXX+PEiRMyabOpmRk8EhOReewYCrW10X3JEsQbGiKThoeUBi1srvg4sRgXp09HiakpHBITEd2vH0weP0bX2bNlliEvLw/e3t4wNzfH3r17oaamJrO260sgEKC4uBinT59mHYXIOSooyXsFBQWhqqoKgwcPZh2lRng8HtavX4/PPvsMAoEASUlJMmvbYuBAdMnPR8ysWTAuKECLPn0Q7uKC548eySwDkQ6alKPY7oSF4YqBAVw2bkSmoSEKo6PhcfIkGujoyCxDWVkZBg0ahMrKSgQHB0NbW1tmbUuCjY0NrK2tadibVIsKSvJeQqEQLi4uMDIyYh2lxtTV1bF//35YWFjAy8sL2dnZMmubx+fDec0a6OXlIcbNDU4xMXhqZISL06aBoyFThUXLBimm548eIdzZGS29vKD/7BkSliyBU04OWvboIdMcYrEY48aNQ3JyMoKDg9GyZUuZti8Jr4e9AwMD8eLFC9ZxiByjgpL8x7Nnz3Dq1Cm5nd39Mdra2ggJCQGfz4e3tzeePXsm0/YbGRjAIyICjyMikGloCJfffkOSnh5uC4UyzUEkg3ooFQsnFiN2zhwUGRvDKTYWFz08YPj4MbovWsQkz+LFi3Ho0CHs2bMH3bp1Y5JBEvz9/fH06VOcOnWKdRQix6igJP8RGhqKiooKhbl/8l3GxsYIDQ1FRkYGhg0bBhGD53GburnBKScHl5YtQ9PSUrQdOhQRdnZ4cveuzLOQuqOCUnFknj2LSy1awGn1auTo6+PR+fPwOH8eWs2aMcmzZ88eLF26FCtWrFDYY+lrNjY2sLGxoWFv8lFUUJL/EAqF6NatG1q1asU6Sp3Z2tpCKBTi9OnTmDp1KjhGRYH9ggUwKSxEtKcnuiYnQ2RujqixYyFmUOSS2qMhb/lXVliIcHd3GPbpA6PCQsTNm4fuubkw8/BglunChQuYOHEixo8fjzlz5jDLIUkCgQDHjh2T2kMkiOKjgpL8S2lpKcLCwhRyuPtdffv2xZ9//ok///wTv/76K7Mcmtra8AgNxfPERNxs3Rquu3fjZtOmuL59O7NMpGaoh1K+JSxejMeGhnCJjESsiwv08vLguHw5eHx2p7Y7d+5g0KBBcHJywp9//gkej8csiyT5+/vj2bNnOHnyJOsoRE5RQUn+5eTJkygtLVWKghIAJk6ciPnz5+P777/HkSNHmGZp0bUret65g+RNm8DnOHScNAlR7drh8fXrTHORD6MeSvmUFR2NOCMjdF+yBI91dJB94gQ8LlxAI8bPxH7y5Am8vb3RrFkzBAQEQFNTk2keSbK2tkbHjh1x+PBh1lGInKKCkvxLQEAAOnbsiHbt2rGOIjFLly7F8OHD8fnnnyM2NpZ1HHT66itYPnmCyBEjYJOeDk1bW0QMHozK0lLW0cg7qIdSvrx4+hThfftCz9UVZo8eIWbmTHR9/Bht+vdnHQ2VlZUQCATIzc1FSEiI3D8Qoi5eD3uXlZWxjkLkEBWU5I2KigoEBwcrTe/ka3w+Hzt37oS9vT0GDhyIjIwM1pGgpqkJt337gFu3kNShA1wDA5Gpp4crDIfmyX9xoB5KeZG4ciVyDAzQ48wZxNvbQ/vBAzivXct0ePs1juMwbdo0nD9/HgEBAbCysmIdSSr8/f1RUlJCw97kvdj/SyRy4+zZsyguLla6ghIAtLS0EBgYiGbNmsHLywuFhYWsIwEA9Cwt4ZaSgtv79qFUUxNdZs1CTMuWyI6JYR2NgHoo5UFuQgJiTE3Rbd48FDdqhMzAQHgkJKCJsTHraG9s3LgRf/75JzZv3oxevXqxjiM17du3R6dOnWi2N3kvKijJG0KhEBYWFujYsSPrKFKhr6+PsLAw5OfnY/DgwXK1SG/7ESNg++QJLkyZgra5udB1cUF4794of/KEdTSVRj2U7FSWliLcywtNHBxgnpODC199BbvCQlj4+rKO9i+hoaH49ttvMWvWLEyaNIl1HKkTCAQICgqiYW/yH1RQEgCASCRCYGAghgwZojSzEt/HwsICQUFBiIuLw4QJE5gtJ/Q+PD4fPTZvRsPMTMTb26PHuXN49MkniFuwgJ62wwj9V2fj6vr1yNTTg2tYGBLt7NDg7l302LRJLoa335acnIzhw4fDx8cHK1euZB1HJvz9/fH8+XMcP36cdRQiZ+TrXydhJjIyEgUFBUo53P0uFxcX7N69G/v27cOPP/7IOs5/6LRsCY+EBDwIDcWjpk3huHw5Lhka4i7dtyRzHI8HHhXzMvPw6lVcaN0anWfORJmGBtIPHID71atoambGOtp/5OXlwdvbG5aWlti7dy/U1NRYR5KJdu3aoXPnzjTsTf6DCkoC4OXsbjMzM9jb27OOIhMCgQArV67E0qVLsXPnTtZx3qutpye6PXqEuLlz0eLJE5gMGIBwR0c8y8lhHU1liAEob3+9/BCVlyPCzw9aXbrA6v59RI0fD5uiIlgNG8Y62nuVlZXB19cXVVVVCA4ORuPGjVlHkimBQIDg4GCU0soU5C1UUBKIxWIEBATAz89PqYe73zV79mxMnjwZkydPxtmzZ1nHeS8enw/HFStg8PAhLvbqBcf4eJSYmuLC11/TMLgM0D2U0pe8eTMydHXhevQokjp0gFpaGlx37ABfXZ11tPcSi8UYO3Ysrl+/jqCgIJiYmLCOJHP+/v5vHoJByGtUUBLExsYiNzdX4Z83W1s8Hg9//PEHevfuDT8/P6SkpLCO9EEN9fTgce4cCqKjcc/ICD02b0ayri5uHTzIOppSEwM0y1tKHqekIMrSEp2+/hoiNTXc3LULbikp0DU3Zx3to3788UccOXIEe/bsQbdu3VjHYcLCwgJdu3alYW/yL1RQEgiFQhgaGsLFxYV1FJlTV1fHoUOH0Lp1a3h6eiIvL491pI9q2aMHnLOycHnVKmiXl8Ni+HBE2NqiSA7W1lRWqtNnLxtVFRWIHDECGra26JiRgciRI9G+sBAdxoxhHa1a//zzD5YtW4aVK1di8ODBrOMwJRAIEBISgufPn7OOQuQEFZQqjuM4CIVCDB48WGVuKn+Xjo4OQkNDIRKJ4O3trRAHyK6zZ8OsqAhRAweiy/XrEFtaInLUKFRVVLCOplRoHUrJStm5E7d0deF24ACuWVigKiUFbnv3Qk0BHlEYHR2NSZMmYcKECfj+++9Zx2HO398fZWVlCA0NZR2FyAkqKFXclStXkJmZqRKzuz+mZcuWCA0Nxa1btzBy5EhUVVWxjlQtjUaN4HHsGMqTknCjbVu47duH282a4dqWLayjKQ0xj0f3UEpAYVoaIq2tYT1hAvgch2tbtsD19m0YWFuzjlYjGRkZGDRoEFxcXLB582aVutf8Q9q2bQt7e3sa9iZvUEGp4oRCIfT09ODu7s46CnOdO3fGwYMHERISgm+//ZZ1nBpr3qkTeqan49qWLeB4PNhOmYJoc3M8vHqVdTSFR5Ny6kcsEiFq3DhwVlawu3kTUUOGwKKwELaTJ7OOVmNPnjyBt7c39PT0IBQKoakAvamyIhAIEBoaipKSEtZRiBygglKFvR7uHjhwIDQ0NFjHkQuenp74448/sHHjRmzYsIF1nFqxnTwZVkVFiPz8c1jfvYuGXbog3McHFXSwrzOaR193N/fvR4quLlx37cKNNm3wIikJ7keOQF1Li3W0GqusrIS/vz8ePnyIkJAQ6OnpsY4kV/z9/VFeXo6QkBDWUYgcoIJShaWmpuLWrVsqP9z9rilTpmDWrFmYOXMmjh07xjpOrahpasJt927w09JwxdYWriEheKCvj0QVeYqHpFEPZe0VZ2YiolMnWI4cCa3KSiRt3IieGRlo3qkT62i1wnEcpk6divDwcAiFQrRr1451JLnTunVrODg40LA3AUAFpUoTCoVo0qQJ+vbtyzqK3Fm1ahX8/PwwYsQIJCQksI5Ta7rm5nBPTkb6wYMo0dJCt3nzEGtsjKzoaNbRFAoHmuVdU5xYjOjJk1HRpg26XruGKG9vtC4shN3Uqayj1cn69euxZcsWbNmyBb169WIdR24JBAKEhYXh2bNnrKMQxqigVGEBAQHw9vZGgwYNWEeRO3w+H//88w/s7Ozg4+ODe/fusY5UJ1YCAToVFeHiN9+g1cOH0Hd1RbiHB8oKC1lHUwjUQ1kzt4VCJOvqoufWrUhr2RIlCQnwCA6GRqNGrKPVSXBwML777jvMnj0bEyZMYB1Hrg0dOhQvXrxAcHAw6yiEMSooVVRGRgaSkpJouPsjGjZsiGPHjqFx48bw8vLCkydPWEeqEx6fD5fffkOT7GzEOTnBJSICBc2bI3b2bHraTjVolvfHPcvJQbi9PdoOHYom5eW4vGoVXO7fh5ECP8I1KSkJI0aMgK+vL1asWME6jtxr1aoVnJycaNibUEGpqoRCIRo2bIgBAwawjiLXmjdvjrCwMOTm5mLIkCGoUOB1HrVbtIBHTAyyT5xArp4enNasweVPPsEdenzaB9GQ9/txYjEuTpuGElNTOCQmIrpfP5g8foyus2ezjlYvubm58Pb2hpWVFfbs2QM+n06RNSEQCHD8+HE8ffqUdRTCEP1rUVFCoRADBgxA48aNWUeRe1ZWVggMDER0dDQmT54MTsF7rNr07w/7vDzEL1wIg6dPYerlhXB7ezzNymIdTS5RD+W/3QkLwxUDA7j89hsyDQ1RdPEiPE6eRAMdHdbR6qW0tBS+vr4Qi8UICgqiY2MtDB06FBUVFQgKCmIdhTBEBaUKevDgAeLj42m4uxbc3Nywc+dO7Nq1C8uWLWMdp954fD4cli6F4ePHuNCnDxwSE1HWqhWiJ0+GWCRiHU9uiHk8elLOK88fPUK4szNaenlB/9kzJCxZAqecHJg4O7OOVm9isRhjx45FSkoKgoODYWJiwjqSQjE1NYWLiwsNe6s4KihVUEBAADQ0NODt7c06ikIZOXIkli5dikWLFmHPnj2s40iEVrNm8Dh9Gk9iY5FhYoKeW7ciRVcXN/buZR1NLtCQ98vh7djZs/HE2BhOsbG46OEBw8eP0X3RItbRJOaHH36AUCjE3r170bVrV9ZxFJJAIMDJkycV9l5zUn9UUKqggIAA9O3bF02bNmUdReEsWLAA48ePx4QJExAREcE6jsQYOzrC5f59XPn1V2hVVsJq9GhEWluj4NYt1tGYEkO1h7wzz57FJUNDOK1Zg2x9fTw6fx4e589Dq1kz1tEkZteuXVi+fDlWrVqFQYMGsY6jsGjYm/A4Rb8hjNTKw4cPYWRkhG3bttFyGHVUWVmJzz77DImJiYiJiUH79u1ZR5KoytJSXBw9Gp2PHoWYx0OyQICeu3dDTQUfOReuoYFnGhrwKS1lHUWmygoLETd4MJwiI5Gvpobs2bPhsGwZeEo2SSUqKgq9e/fGmDFjsHXrVnpGdz25urqiadOm9OQcFaVcRwdSrcDAQPD5fAwcOJB1FIWloaGBI0eOwMTEBJ6ennj06BHrSBKl0agR3AMCUHH9Oq5bWMD94EGkN22KpN9/Zx1N5lRx2aD4RYvw2NAQLpGRiHVxgV5eHhyXL1e6YjI9PR2DBw9Gz549sWnTJiomJcDf3x+nTp1CUVER6yiEAeU6QpBqCYVCuLu7w8DAgHUUhdasWTOEhoairKwMAwcORFlZGetIEveJjQ1cb99Gyo4dEKmpwW7qVFxo3Rq5ly6xjiYzqnQPZVZ0NOKMjOCwdCke6+gg+8QJeFy4gEZKeKwoKiqCt7c39PX1ceTIEWiqYO+7NAwZMgQikUjhHllLJIMKShVSWFiI8+fP0+xuCWnVqhWCg4Nx7do1jB49GmIlXSTcZvx4WD95gqixY9Hu/n006d4d4Z6eqCgpYR1NJpS9h/LF06cI79sXeq6uMHv0CDEzZ6Lr48do078/62hSUVlZiaFDh+Lx48cICQmBnp4e60hKw8TEBD179sThw4dZRyEMUEGpQoKDg1FVVYXBgwezjqI07O3tsX//fhw9ehSzFXxR54/hq6vD9e+/oXn3LhLt7NDz+HFk6+nhkhIsofQxYh5PqXsoE1esQI6BAXqcOYN4e3toP3gA57VrlW54+zWO4/DNN98gKioKAQEBsLS0ZB1J6QgEAhr2VlHKedQg7yUUCuHi4gIjIyPWUZTKwIEDsWHDBvz666/YtGkT6zhS1bRVK7hfvYo7R47gSePGsP/hB8QZGeF+eDjraFKjjAVlbkICYlq2RLf581HcqBEyAwPhkZCAJsbGrKNJ1bp16/DXX39hy5YtcHd3Zx1HKQ0ZMgRVVVUIDAxkHYXIGBWUKuLZs2c4deoU/Pz8WEdRSlOnTsX06dMxdepUhIaGso4jde2GDEHnggJcnDEDpo8fo3mvXgh3dUVpfj7raBKlbMsGVZSUINzLC00cHGCem4sLX30Fu8JCWPj6so4mdUFBQZg1axbmzJmD8ePHs46jtIyMjODm5kaLnKsgKihVRGhoKF68eEEFpRT9+uuv8PHxwbBhw3DlyhXWcaSOx+fDZd06NM3JQWyPHnCOjkZRixaI+fZbcEpyPynH4ynNQfLq+vV4oK8P17AwJNrZocHdu+ixaZPSDm+/7erVqxg5ciQGDx6M5cuXs46j9AQCAc6cOYOCggLWUYgM0TqUKsLf3x93797FJRWaocvC8+fP4eHhgezsbMTFxcHU1JR1JJnJPHsWD0eOhMOjR7isqwudnTsVvufruJYW1DkOfV+8YB2lzh5evYr0QYPQIzMTyU2aoMG2bbASCFjHkpnc3Fw4ODjA0NAQERER9IxuGcjLy4OJiQn++usvTJw4kXUcIiPKf2lKUFZWhrCwMJrdLQONGzdGcHAwNDU14eXlhadPn7KOJDOteveGw8OHSFi8GHolJWg9aBAiunZF8f37rKPVmSIvGyQqL0fE4MFo2KULrO7fR9T48ehYWKhSxWRpaSkGDhwIjuMQFBRExaSMtGjRAu7u7jTsrWKooFQBJ0+eRGlpKRWUMtKiRQuEhYXh/v378Pf3R2VlJetIMtX9xx9hlJ+P6P790e3KFVS0bo2oCRMgFolYR6s1TkEXNk/evBkZurpwDQzE1Q4doJaWBtcdO8BXV2cdTWbEYjE+//xzpKamIjg4GMZKPuFI3ggEApw9exb5SnZfNfkwKihVgFAohI2NDdq1a8c6isro0KEDAgICcO7cOXz99ddQtTtLGujowOPECTyLj8dtMzO47tyJ1GbNkLprF+totaJoPZSPU1IQZWmJTl9/DZGaGm7u2gW3lBTompuzjiZzCxcuxNGjR7F//3506dKFdRyV4+fnB47jcPToUdZRiIxQQankKioqEBwcTL2TDHz66afYtm0btm3bhlWrVrGOw4RR9+7oce8erq5fD82qKrQfNw5RVlbIv3GDdbQa4Xg88BXgYqCqogIRw4dDw9YWHTMyEDlyJNoXFqLDmDGsozHx999/Y8WKFVizZg09ZpaR5s2bo1evXjTsrUKooFRyZ8+eRXFxMRWUjIwdOxaLFi3CvHnzcPDgQdZxmOk8fTraFhUhauhQ2KalQaNDB0QMGQJReTnraB8l/6UkkLJzJ27p6sL94EFcs7REVUoK3PbuhZqKPk4wIiICkydPxqRJk/Dtt9+yjqPSBAIBzp07h8ePH7OOQmSACkolJxQKYWFhAVtbW9ZRVNbixYsxevRojB07FhcuXGAdhxl1LS24Hz4MUUoKktq3h2tAAO7o6uLqhg2so32QPC8bVJiWhkhra1hPmAA+x+Hali1wvXULBtbWrKMxk5aWBj8/P7i6umLTpk3g8RTphgXlM3jwYPB4PAQEBLCOQmSAlg1SYiKRCEZGRpg4cSJWrlzJOo5Ke/HiBfr374/r168jJiaGHvkGIHX3blT973+wLSnBRVNTtBEKYdS9O+tY/xLYuDH0KyrgKkcTq8QiES5MnIgO//wDdY7D1SFD0GPPHqhrabGOxlRRURGcnJzA4/EQExMDXV1d1pEIgH79+qGqqgpnz55lHYVImbxefBMJiIqKQn5+Pi1mLgcaNGiAo0ePwsDAAJ6enjTzEUCHMWNgU1SE6IkTYZmVBR0HB4T364cX8rTUkpzN8r65fz9SdHXhuns3brRpgxdJSXA/ckTli8nKykoMHToU+fn5CAkJoWJSjggEAoSHh+Phw4esoxApo4JSiQmFQpiamqK7nPX6qCpdXV2EhYWhuLgYgwYNQrmc3z8oC3x1dfTctg2a9+4hoWtX9Dh9Gnn6+khYvJh1NADyM8u7ODMTEZ06wXLkSGhVViJp40b0zMhA806dWEdjjuM4fP3114iKisLRo0dhYWHBOhJ5Cw17qw4qKJWUWCxGQEAA/Pz86D4iOdK2bVsEBwcjMTER48aNg1hJHlFYX03NzOCRmIjMY8dQ0KQJui9ZgnhDQ2QyHibjeDymBSUnFiN68mRUtGmDrteuIcrbG60LC2E3dSrDVPJl7dq12LZtG7Zu3Qo3NzfWccg79PX10adPH5rtrQKooFRSsbGxyM3NpdndcsjR0RF79uzBoUOHsHDhQtZx5IrFwIHokp+PmFmzYFxQgBZ9+iDcxQUleXlM8rBcNui2UIhkXV303LoVaS1boiQhAR7BwdBo1IhJHnl07NgxfP/995g3bx7Gjh3LOg75AIFAgIiICOQx+ndMZIMKSiUVEBAAQ0NDuLi4sI5C3mPIkCFYs2YNVqxYga1bt7KOI1d4fD6c16yBXl4eYtzc4BQTg2cmJrg4bRo4GffosuihfJqVhXB7e7QdOhRNystxedUquNy/DyN7exknkW9XrlzByJEj4efnh2XLlrGOQz5i0KBBUFNTg1AoZB2FSBHN8lZCHMehbdu26N+/P/7880/WccgHcByHb775Blu2bEFYWBj69evHOpJcuh8ejtwRI+CYl4erTZui0bZtaDd0qEzaPty0KVqVlMChqkrqbXFiMWJmzEDbP/6AjliM+H794Hz4MBro6Ei9bUWTnZ0NR0dHGBkZISIiAo2o11bueXp64vnz54iIiGAdhUgJ9VAqoStXruDevXs03C3neDweNmzYgP79+2Po0KFITk5mHUkumXl4wDE3F5eWLUPT0lKY+/sjws4OT+7elXrbsuqhvBMWhisGBnD57TfcMzRE0cWL8Dh5korJ93j+/DkGDhwIHo+HoKAgKiYVhEAgQFRUFHJyclhHIVJCBaUSEgqF0NXVhYeHB+sopBrq6uo4ePAgLCws4OXlRQfbj7BfsAAmhYWI8vRE1+RkiMzNETV2LMQikVTbleZB8vmjRwh3dkZLLy/oP3uGhCVL4JSTAxNnZym2qrjEYjHGjBmDW7duITg4GEZGRqwjkRry9fWFuro6DXsrMSoolQzHcRAKhfD19YWGhgbrOKQGtLW1ERISAgDw9vZGSUkJ40TyS1NbGx6hoXiemIibrVvDdfdu3GzaFNe3b5dOg1LqoeTEYsTOno0nxsZwio3FxV69YPj4MbovWiSF1pTH/PnzcfToUezfvx+dO3dmHYfUgq6uLvr160ezvZUYFZRK5saNG7h16xYNdysYY2NjhIaGIj09HcOHD4dIyr1uiq5F167oeecOkjdtAp/j0HHSJERZWuLx9esSbUcshYXN750+jUuGhnBaswbZ+vp4dP48PM6dg1azZhJtR9ns3LkTq1atwi+//AIfHx/WcUgdCAQCREdHIzs7m3UUIgVUUCoZoVCIJk2aoE+fPqyjkFrq1KkTjhw5ghMnTmDatGmg+XLV6/TVV7B88gSRI0bAJiMDmra2iBg8GJWlpfXe9/OCJ6jU/gQF+qbIOBWF5wVP6rW/ssJChLu5wahfPxgVFSFu3jx0z82FGd2aUq3w8HBMnjwZkydPxsyZM1nHIXU0cOBAaGho4MiRI6yjECmgWd5KpnPnzrC2tsb+/ftZRyF1tHXrVkyePBm//PILvvvuO9ZxFEZhWhquDxqEnqmpuNOgAZ79/DO61PK/X2ZkAvJWr0fL2HAYFeT864pbDCBX3xhZTh5oMXsGWrnV/AlU8YsWocWKFWghEuGiiwscjh1DIwODWmVTVWlpaXByckKXLl1w/PhxupVHwfn4+KCwsBAXLlxgHYVIGkeURnp6OgeAO3z4MOsopJ7mzp3L8Xg87siRI6yjKJwb+/ZxSU2acBzAXTQx4bIuXqx2m+zLKVxyBweOA7hKHp/jgA/+ef1+cgcHLvtyykf3ez8igott0YLjAO6Snh5358QJSf2aKqGgoICztLTkrKysuMLCQtZxiATs3r2bA8Ddv3+fdRQiYTTkrUSEQiEaNmyIzz77jHUUUk8///wzBAIBRo8ejdjYWNZxFEr7ESNg++QJor/8Em1zc6Hr4oLw3r1R/uTJez8fv2AV9By6wPrGJQCAOvfxxdNfv2994xL0HLogfsGq/3zmxdOnCO/TBwbu7jB79AgxM2ei6+PHaNO/f/1+ORVSUVGBoUOHorCwEKGhodDV1WUdiUjAwIEDoampSbO9lRANeSsRR0dHmJiYICAggHUUIgHl5eXo06cPbt++jdjYWLRt25Z1JIXzNCsLlwcPRo9Ll5Crro7c77+Hw7Jl4PFfXkvHTPwWzjvWgQPqNJv79XYxE2bCeftaAEDiihXQ+/FHtKysxAV7e3Q7dgxNjI0l9SupBI7j8MUXX2D37t04e/YsXF1dWUciEuTr64vHjx/j4sWLrKMQCaKCUkk8ePAAZmZm2LNnD0aNGsU6DpGQ/Px8ODs7Q01NDRcvXoSenh7rSArpTlgYCseMgX1BAS7p60Pvn3+QH50Mh+VzJdZG+OTv0SB0H5yzs3G1aVNo79oFC19fie1flaxZswazZ8/Grl27MGbMGNZxiITt3bsXo0ePRmZmJszMzFjHIRJCQ95K4ujRo9DQ0ICXlxfrKESCDAwMEBYWhvz8fPj5+eHFixesIymktp6e6PboEeLmzoVhcTHUho1Bp9WL8KGr6acAHAGo4WUPpDaAlR/ZPwfAaccG6DyrwIWvvoJdYSEVk3UUGBiIOXPmYP78+VRMKikfHx80aNCAZnsrGeqhVBLu7u5o3LgxwsLCWEchUnDhwgX07t0b/v7+2L17N3g8WTwQUDmVFRbihl0PdMq+/cH7JVsBuA/AHoAVgGMASgD8AeDrD+xXxOMjxaor7G4kSCG1arh8+TJcXV3h6emJgwcPgs+nPg9lNXjwYOTm5tI94kqE/rUqgYcPHyIqKooWM1diPXr0wK5du7Bnzx4sXryYdRyF9uh6Brpm3fxgMbkTL4tJLwAJAPYAeABAHcCcj+xXnRPD7uYlZEZfknBi1ZCdnQ0fHx/Y2Nhg165dVEwqOYFAgLi4ONy7d491FCIh9C9WCQQGBoLP58OXhtiU2rBhw7BixQr89NNP+Pvvv1nHUVh5q9dDxPvwoW/zq69/vvWzZgA+xcteyriP7FvE4yNv5br6RlQ5z58/h4+PD9TU1HDs2DE0atSIdSQiZd7e3tDS0qJhbyVCBaUSCAgIgLu7OwxooWSlN2fOHHzxxRf44osvcPbsWdZxFFLL2PCPLg2UAUATQMt3fv762VMhH9m3OieGSWxE/QKqGLFYjNGjRyMtLQ3BwcEwMjJiHYnIQJMmTeDp6UnP9lYiVFAquKKiIpw7dw5+fn6soxAZ4PF4+OOPP/Dpp59iyJAhSE1NZR1JoZTkF8GoIOejn3kOoPF7fm716uudatowLsiu92MaVcm8efMQFBSE/fv3w87OjnUcIkMCgQAJCQm4e/cu6yhEAqigVHBBQUEQiUQYPHgw6yhERjQ0NHD48GGYmZnB09MTeXl5rCMpjIeXr1d70KvCy/sl36Xz6mt1TwnnA8hLvFbbaCpp+/btWL16NX799Vd4e3uzjkNkzMvLCw0bNsThw4dZRyESQAWlghMKhXBxcYExLZysUnR0dBAaGorKykr4+Pjg+fPnrCMpBFFZebWfUQMges/Pn776WpO7+2rSjqo7f/48pkyZgi+//BLTp09nHYcwoK2tDS8vLxr2VhJUUCqwZ8+e4dSpUzS7W0WZmpoiJCQEN27cwKhRo1BVVcU6ktxTb6hV7Wca4+Ww97tuvfpak+cV1aQdVXb79m0MGTIEvXr1wm+//UbLYKkwgUCAxMREZGRksI5C6okKSgUWFhaGFy9e0P2TKqxLly44dOgQgoOD8d1337GOI/dadLPFx5/U/bJgrACQ9c7PT7/6Wt3ArPhVO+T9CgsL4e3tDUNDQxw6dAgaGhqsIxGGPD090ahRIxr2VgJUUCowoVCIrl27onXr1qyjEIY8PT3x22+/YcOGDdi4cSPrOHKtsX4z5Op//PaQKe98BV4Od4fjZe+lYzVt5OiboLF+szomVG4VFRXw8/NDYWEhQkJC0KxZM9aRCGONGzeGt7c3DXsrASooFVRZWRnCwsJouJsAAL7++mt89913mDFjBoKCgljHkWtZTh4fXYdyIl4uGRQKwAHA6FffVwJYUc2+RTw+sp3cJZRUuXAchylTpiAmJgaBgYEwNzdnHYnICYFAgCtXriAtLY11FFIPVFAqqJMnT+L58+dUUJI3Vq9eDT8/P4wYMQKXLtHTWj4ku4/bR9ehBIBrePnYxUQAe/FyGHsZgKnV7FudE4M/YZQkYiqdNWvWYOfOndi+fTt69uzJOg6RI5999hkaN25Mw94KjgpKBSUUCmFjYwMrK6vqP0xUAp/Pxz///ANbW1t4e3sjMzOTdSS5cu3aNfTv3x9DZk5BtEn7j/ZSNsPLxy5WAeDw8gk5C6rZv4jHR4SRJVxH+uH7779HUVGRpKIrvKNHj2Lu3LlYuHAhRo8ezToOkTONGjWCj48PDXsrOCooFVBFRQWCg4Opd5L8R8OGDREUFIRGjRrBy8sLT548YR2Juby8PHzxxRfo3Lkz7ty5g4CAALQJOgKRmjo4CbXBARCpqaPlkX2YN28eNm/ejLZt2+KXX35BeblqLyGUmJiIUaNGYejQoViyZAnrOEROCQQCJCUl4datW9V/mMglKigV0Llz51BcXEwFJXmv5s2bIywsDDk5ORg6dCgqKipYR2KitLQUy5Ytg4WFBYRCIdauXYuUlBQMHjwYJl1tkDz7J0hqsRoegOQ5S2HuYo8ff/wR6enpGD58OObOnQsrKyv8888/EIurm1+ufLKzszFw4EDY2tpi165d4PPplEPeb8CAAdDW1qZhb0XGEYUzadIkztzcnBOLxayjEDkWHh7OaWhocOPHj1epvytVVVXcrl27OBMTE05DQ4P79ttvucLCwvd+9uKEmRwHcGKA4+rw5/V2MRO/fe/+b968yQ0ePJgDwHXu3Jk7deqUNH91ufLs2TOuS5cunKmpKZebm8s6DlEAI0eO5GxtbVnHIHVEBaWCqays5AwMDLjZs2ezjkIUwD///MMB4JYuXco6ikycP3+e69q1KweAGzJkCJeenl7tNnHzV3Jl6ppcJY9fq2KyksfnytQ1ubgFq6ptIzo6mnNxceEAcH379uUuX74siV9XbolEIs7X15fT1tbmkpKSWMchCiIwMJADwKWmprKOQuqACkoFc+7cOQ4AFxcXxzoKURA//fQTB4Dbs2cP6yhSc/PmTW7gwIEcAM7BwYGLioqq1fbZl1O45A4ObwrF6gpJDuCSOzhw2ZdTatyGWCzmjh49yrVr147j8Xjc6NGjuXv37tX2V1UIs2bN4vh8PhcSEsI6ClEgZWVlXJMmTbglS5awjkLqgApKBfO///2PMzU1VakhTFI/YrGYGzduHKepqclFRESwjiNRjx8/5r755htOXV2da9WqFbdv3z6uqqqqzvu7FxHPxXqN5B7om3BV7xSSVQD3QN+Ei/Uayd2LSqhzGxUVFdzmzZs5Q0NDTlNTk/vuu++4goKCOu9P3mzdupUDwK1fv551FKKARo0axdnY2LCOQeqACkoFUlVVxRkbG3PTp09nHYUomBcvXnCffvopp6ury928eZN1nHorLy/nVq9ezTVt2pTT0dHhVq5cyZWVlUm0jZL8Im7jjLncp8YWXPrJSK4kv0ii+3/27Bm3ePFirnHjxlyzZs24NWvWSPx3kLWzZ89y6urq3FdffUUXvaROjh07xgHgrl+/zjoKqSWacqdA4uLikJOTQ8/uJrWmqakJoVAIIyMjeHp64vHjx6wj1QnHcTh06BDat2+PefPmYdSoUUhPT8ecOXOgpaUl0bYa6zeDuHULxBRlw7yfq8Qfp6itrf1mRviIESMUfkb4rVu3MGTIEPTq1QsbNmwAjyepOfRElfTr1w86Ojo021sBUUGpQIRCIZo3b44ePXqwjkIUULNmzRAaGornz59j4MCBKCsrYx2pVmJiYtCjRw8MGzYMHTt2xLVr1/DHH3/gk08+kVqbfD4fHCep1Srfr0WLFti0aRNSUlJgb2+PMWPGoGvXrjh16pRU25WkgoICeHl5wcjICIcOHYKGhgbrSERBaWlpwdfXlxY5V0BUUCoIjuMgFAoxePBgqKmpsY5DFFTr1q0RHByMpKQkfP755wrRE3b37l0MGzYMLi4uKC0txZkzZxAcHAxra2upt83j8aReUL5mZWUFoVCICxcuQFtbG/3790ffvn1x5coVmbRfVxUVFfDz80NxcTFCQkLQrFkz1pGIghMIBLhx4wZSUlJYRyG1QAWlgrhy5Qru3btHi5mTeuvevTv279+PgIAAzJkzh3WcD3ry5Am+//57tG/fHlFRUdixYwcSExPRu3dvmWXg8XgyL7pdXFwQFRWFo0eP4sGDB+jatStGjx6Ne/fuyTRHTXAchy+//BKxsbEIDAxE27ZtWUciSqBv375o2rQp9VIqGCooFURAQAB0dXXh4eHBOgpRAr6+vli3bh1++eUXbN68mXWcf6msrMRvv/0GCwsLbNq0CfPnz0daWhrGjx8v8955WQx5vw+Px8OgQYNw/fp1/Pnnnzhz5gysrKwwa9YsFBYWyjzPh6xatQp///03duzYQbfiEIlp0KABBg0ahEOHDjH590fqiOGEIFIL7du358aOHcs6BlEy06ZN4/h8PhcaGso6CicWi7nAwMA36zROmDCBy87OZppp8+bNHJ/PZ5qB417OCF+yZMmbGeGrV69mPiNcKBRyALgffviBaQ6inEJDQzkAXHJyMusopIaoh1IBpKam4ubNmzTcTSRu7dq18Pb2hkAgYHqvXmJiInr16oVBgwbBzMwMV65cwfbt22FsbMwsE8Cuh/Jd2traWLRoETIyMjBy5EjMmzcP7dq1w+7du1FVVSXzPJcuXcLo0aMxbNgwLFmyRObtE+XXp08fNGvWjIa9FQgVlApAKBRCW1sbffv2ZR2FKBk1NTXs27cP7du3h7e3N7KysmTa/oMHDzBmzBjY29vj0aNHCA0NxalTp2BnZyfTHB8iy0k5NWFoaIg//vgDqampcHBwwNixY9GtWzecPHlSZjmzsrIwcOBAdOrUCTt37qTlgYhUaGpqYvDgwTTsrUCooFQAQqEQ3t7eEl9njxAAaNy4MUJCQqCurg4vLy88ffpU6m0+e/YMCxcuRLt27XDixAls3rwZycnJ8PT0lKsChc9/eYiUtxNau3btcOTIEVy8eBHa2toYMGAA+vXrJ/Ve5pKSEvj4+EBDQwPHjh1Dw4YNpdoeUW0CgQC3b99GcnIy6yikBqiglHMZGRlISkqi4W4iVS1atEBYWBju3bsHgUCAyspKqbQjEonw119/wdLSEr/88gtmzpyJ9PR0TJkyBerq6lJpsz5eF7fyVlC+5uzsjKioKAQGBkp9RnhVVRVGjRqFjIwMhISEwNDQUOJtEPK23r17Q1dXl4a9FQQVlHIuICAAWlpaGDBgAOsoRMnZ2NggICAAZ8+exf/+9z+JF1EnT55Ely5d8OWXX6JPnz64ffs2li9fDh0dHYm2I0mveyjleb1OHo8HX19fXL9+HVu2bMHZs2dhZWWF7777TqIzwufMmYOQkBAcOHAAtra2EtsvIR+ioaEBPz8/GvZWEFRQyjmhUIgBAwZAW1ubdRSiAnr37o2//voLW7duxerVqyWyz+vXr2PAgAEYMGAAdHV1ER8fjz179sDMzEwi+5cmee+hfJu6ujomT56MtLQ0LFiwAH/99RfMzc2xevXqej8VaevWrfj111+xbt06eHp6SigxIdUTCARIT0/H1atXWUch1aCCUo5lZWUhLi6OhruJTI0fPx4LFy7E3Llz6zXUlJeXh8mTJ8POzg4ZGRkQCoWIiIhA9+7dJZhWul4XlPLcQ/mu1zPC09PTMXLkSCxYsABWVlbYtWtXnWaEnz17Fl9//TW+/vprTJ06VQqJCfmwXr16QV9fn4a9FQAVlHIsICAAGhoa8Pb2Zh2FqJiffvoJI0eOxJgxY3DhwoVabVtaWoply5bB0tISR44cwa+//oqUlBT4+fnJ1YSbmpDXSTk18XpGeEpKChwcHDBu3Dh07doVJ06cqPHv83q5st69e2PDhg0K9/+PKD4a9lYcVFDKMaFQ+GYtLkJkicfjYceOHXB0dISvry/S09Or3UYsFmP37t2wsrLCTz/9hC+++ALp6emYMWMGNDU1ZZBa8hSxh/Jdb88I19HRwWeffYa+ffvi8uXLH90uPz8f3t7eMDExwcGDB+Vy0hRRDQKBAHfu3Kn27yxhiwpKOfXw4UNERUXRcDdhpkGDBjh69CgMDAzg6emJgoKCD342PDwc3bt3x9ixY+Ho6IjU1FSsXbsWenp6MkwseYrcQ/kuZ2dnREZGIjAwENnZ2ejWrRtGjRqFu3fv/uezL168gJ+fH54+fYqQkBA0bdqUQWJCXvLw8ICBgQENe8s5Kijl1LFjx8Dj8TBw4EDWUYgK09PTQ2hoKIqKijBo0CCUl5f/6/3bt29j0KBB6NWrF9TV1REVFYUjR47AwsKCUWLJUqRJOTXxekb4tWvXsGXLFpw7dw7t27fHd9999+aCgeM4fPnll4iPj0dgYCDatGnDODVRderq6hgyZAgNe8s5KijllFAohLu7Oz755BPWUYiKMzc3R1BQEC5duoTx48dDLBYjPz8f06ZNg42NDa5cuYJ9+/YhJiYGPXv2ZB1XohRh2aC6eD0jPD09HQsXLvzXjPClS5di165d2LFjB1xcXFhHJQTAy2Hve/fu4dKlS6yjkA+gm2LkUFFREc6dO4f169ezjkIIgJfDpf/88w/8/f3x6NEjJCYmQiwWY+nSpZg+fbrSPjFF2Xoo39W4cWP88MMP+PLLL/HTTz9h3rx5EIvF8PX1xbBhw1jHI+QNNzc3NG/eHIcOHVKolSJUCfVQyqGgoCCIRCIMHjyYdRRCALwsqMRiMfT09HDu3Dl07twZ6enpmDt3rtIWk4ByTMqpiebNm2Ps2LHQ0NCAqakpjh07hi5dutRqRjgh0kTD3vKPCko5FBAQABcXFxgbG7OOQghiYmLQo0cPDBs2DE5OThg2bBiio6NVYqFhZZqU8zEPHjzAwIED0aVLF9y+fRsxMTFo1qxZjWeEEyILAoEA9+/fR3x8POso5D2ooJQzz549w8mTJ+Hn58c6ClFxd+/exbBhw+Di4oLS0lKcOXMGoaGh2LNnD/r164ehQ4fi2rVrrGNKlSr0UJaUlMDHxwcNGjRAYGAgtLS04OTkhIiICBw7dgw5OTkfnRFOiKy4urrC0NCQZnvLKSoo5UxYWNibJTsIYeHJkyeYPXs22rdvj6ioKOzYsQOJiYno3bs3gJdDTwcPHoS5uTm8vLyQk5PDOLH0KHsPZVVVFUaMGIE7d+4gJCQEhoaGb957vcpEcnIy/vrrrzczwr/99tuPLiFFiLSoqalh6NChOHz4sFJf5CkqKijljFAoRNeuXWmpDiJzlZWV+P3332FhYYE//vgD8+fPR1paGsaPHw81NbV/fbZJkyYICQmBWCyGt7c3SkpKGKWWLmXvofz+++8RFhaGQ4cOoWPHju/9jLq6+ptF6n/44Qds3boV5ubmWLVqVb2fEU5Ibfn7++PBgweIi4tjHYW8gwpKOVJWVoawsDBazJzIFMdxCAoKgq2tLaZNm4aBAwciLS0NP/74Ixo3bvzB7UxMTBAWFob09HQMHz4cIpFIhqllQ5l7KLds2YJ169Zhw4YNGDBgQLWfb9y4MRYuXIiMjAx8/vnnWLhwIdq1a4e///67Ts8IJ6QuevbsiRYtWtCwtxyiglKOnDx5Es+fP6eCksjM5cuX8emnn8LX1xctW7bE5cuXsWPHjhpPCOvUqRMOHz6MEydOYMaMGUpXeCnrskFnzpzB//73P3zzzTf45ptvarVt8+bN8dtvvyE1NRXOzs4YP348unTpguPHjyvdfycif2jYW35RQSlHAgIC0KFDB1hZWbGOQpRcVlYWxo4dC3t7ezx8+BAhISE4ffo0OnfuXOt99e/fH5s2bcIff/yhdGunKuOQ940bNzB06FD07dsX69atq/N+LC0tcejQoTczwj09PdGnTx8kJiZKMC0h/yUQCJCdnY2YmBjWUchbqKCUExUVFQgKCqLeSSJVJSUl+OGHH9CuXTscP34cf/zxB5KTk+Hl5fWmeKqLyZMnY86cOfjuu+9w9OhRCSZmS9mGvPPz8+Ht7Y2WLVviwIEDUFev/7MtXs8IDwoKQm5uLuzt7TFy5EiaEU6kpkePHjAyMqJhbzlDBaWcOHfuHIqLi6mgJFJRVVWFbdu2wcLCAmvWrMH06dORlpaGr776SiJFBQAsX74c/v7+GDVqlNLcMK9MPZQvXrzA4MGD8ezZM4SEhKBp06YS2zePx4OPj8+bGeHh4eGwsrLCzJkzaUY4kTg+nw9/f38cOXJEKf5tKgsqKOWEUCiEubk5OnXqxDoKUTKnTp1Cly5d8MUXX6B37964desWVqxYIdGCAnh5kN+1axe6dOkCHx8fpeihUpYeSo7j8MUXXyAhIQHHjh1D69atpdLO6xnhaWlpWLRoEbZv3w5zc3OsXLmSZoQTiRIIBMjJycHFixdZRyGvUEEpB0QiEQIDAzFkyJB6DTsS8raUlBR89tln6N+/P3R0dBAXF4e9e/eiVatWUmtTS0sLx44dQ9OmTeHp6YmioiKptSULytJDuXz5cvzzzz/YuXMnnJ2dpd7e6xnh6enp+Pzzz9/cZrFz506aEU4kwtnZGSYmJjTsLUeooJQD0dHRyM/Pp8XMiUQ8fPgQX375JTp16oS0tDQcOXIEUVFRcHBwkEn7BgYGCAsLw6NHj+Dn54eKigqZtCsNytBDefjwYSxcuBCLFy/GiBEjZNr26xnhN27cgLOzMyZMmIDOnTvTjHBSb28Pe9NFinygglIOCIVCtGzZEt27d2cdhSiwsrIy/Pzzz7CwsMDhw4fxyy+/IDU1lUnPt6WlJY4dO4aLFy9i0qRJCls8KPqyQfHx8RgzZgxGjBiBRYsWMcthYWGBQ4cOITY2Fnp6evD09ETv3r1x6dIlZpmI4hMIBMjNzcWFCxdYRyGggpI5sViMgIAA+Pn5vekNIaQ2xGIx/vnnH7Rr1w5LlizBpEmTkJ6ejpkzZ0JTU5NZrp49e2LXrl34559/sGTJEmY56kORh7zv37+PgQMHokuXLtixY4dc3E7j6OiI8PBwBAUFIS8vD927d3/z6EdCasvR0RGmpqY07C0nqIJhLC4uDjk5OTS7m9RJREQEHBwcMGbMGDg4OCA1NRXr1q2Dnp4e62gAgOHDh2P58uVYsmQJdu3axTpOrSnqkPezZ8/g7e2Nhg0bIjAwEFpaWqwjvfH2jPCtW7ciIiIC7du3x8yZM5Gfn886HlEgNOwtX6igZEwoFKJ58+bo0aMH6yhEgdy+fRuDBw+Gh4cH+Hw+IiMjIRQKYWFhwTraf8ydOxcTJ07EpEmTcO7cOdZxakUReyirqqowYsQIZGZmIiQkBM2bN2cd6b3U1dUxadKkN4/5pBnhpC4EAgEePnyIqKgo1lFUHhWUDHEch4CAAAwaNAhqamqs4xAFUFBQgOnTp8PGxgaXL1/G3r17ERsbC1dXV9bRPojH42Hz5s3o1asX/Pz8kJqayjpSjSliD+WsWbNw/PhxHDp0CDY2NqzjVKtx48ZYsGABMjIyMHbsWPzwww+wtLSkGeGkRhwcHGBmZkbD3nKACkqGrl69irt379JwN6nWixcv8Ouvv8LCwgI7d+7E0qVLcfPmTYwcOVIh7r3V0NDA4cOHYWpqCk9PT+Tl5bGOVCOK1kP5559/Yv369di4cSP69+/POk6tfPLJJ9i4cSNu3LiBHj16vJkRHhYWplAFPZEtHo8HgUAAoVAIkUjEOo5Kk/8zkRITCoXQ1dVFr169WEchcorjOBw+fBgdOnTAnDlzMGLECKSnp2Pu3Llo2LAh63i10rRpU4SGhqKiogIDBw5EaWkp60jVUqQeytOnT+Obb77B1KlT8b///Y91nDqzsLDAwYMHERcXBz09PXh5edGMcPJRAoEAjx49QmRkJOsoKo0KSoaEQiEGDhwIDQ0N1lGIHIqNjUXPnj0hEAhgbW2N5ORkbNq0SW7viasJMzMzhISEIDU1FaNGjZL7IU1FWTboxo0b8Pf3R79+/bB27VrWcSTCwcEB4eHhCA4OxsOHD2lGOPkge3t7tG7dmoa9GaOCkpHU1FTcvHmThrvJf9y7dw/Dhw+Hs7MzSkpKcPr0aYSEhKBDhw6so0lE165dceDAAQQFBWHWrFms43yUIgx5P378GF5eXjA1NcWBAwck9mx2ecDj8eDt7Y2kpCRs27YNkZGRaN++PWbMmEEzwskbNOwtH6igZEQoFEJbWxt9+/ZlHYXIieLiYsyZMwft27dHZGQktm/fjsuXL6NPnz6so0mct7c3Nm7ciPXr1+O3335jHeeD5H3I+8WLFxg8eDCeP3+OkJAQ6OjosI4kFerq6pg4cSLS0tKwePFi7NixA+bm5lixYoVC3DpBpE8gECA/Px/h4eGso6gsKigZCQgIgJeXl1ytD0fYqKysxB9//AELCwv8/vvvmDt3Lm7fvo0JEyYo9ez///3vf/j2228xY8YMBAcHs47zXvLcQ8lxHCZNmoRLly7h2LFjUn1Gu7xo1KgR5s+fj4yMDIwbNw6LFi1Cu3btsGPHDrm/fYJIV9euXdG2bVsa9maICkoG7ty5g6tXr9Jwt4rjOA7BwcGwtbXF1KlT4ePjg9u3b2Px4sXQ1tZmHU8m1qxZA19fXwwfPhyJiYms4/yHPPdQ/vzzz9izZw927doFJycn1nFk6pNPPsGGDRvezAifOHEizQhXca+HvQMCAlBZWck6jkqigpIBoVAILS0tfPbZZ6yjEEauXLmC3r17Y+DAgWjZsiUuX76MHTt2wMTEhHU0meLz+dizZw86duwIb29vZGZmso70L/LaQ3no0CH88MMPWLJkCYYNG8Y6DjNvzwjX19eHl5cXPv30UyQkJLCORhgQCAQoKCjA+fPnWUdRSVRQMiAUCjFgwACV6YUi/y87Oxvjxo1Dt27dkJubi5CQEJw+fRqdO3dmHY2ZRo0aISgoCFpaWvDy8kJxcTHrSG/IYw9lXFwcxo4di1GjRuGHH35gHUcuODg44Pz58wgJCcHjx4/h4OCA4cOHIyMjg3U0IkOdO3eGhYUFDXszQgWljGVlZSEuLo6Gu1VMSUkJFi1aBEtLS4SGhuL3339HcnIyvLy83vSCqTJDQ0OEhYUhOzsbQ4cOlZshK3nroczMzMTAgQPRtWtXbNu2jf7uvIXH48HLywtJSUnYvn07oqKiYG1tjenTp9OMcBVBw95sUUEpY0ePHoWGhga8vb1ZRyEyUFVVhe3bt8PS0hKrV6/GtGnTkJ6ejq+//prWH32HtbU1jh49ioiICEyZMkUuegXlqYfy6dOn8PHxQePGjREYGEgT+j5ATU0NEyZMeDMjfOfOnTA3N8fy5ctpRrgKEAgEKCoqwtmzZ1lHUTlUUMqYUChE79690axZM9ZRiJSdPn0aXbt2xaRJk9CrVy/cvHkTK1euRNOmTVlHk1seHh7Yvn07duzYgeXLl7OOIzcLm4tEIowYMQKZmZkICQnBJ598wjSPInh3RvjixYthaWlJM8KVXKdOndCuXTsa9maACkoZevToEaKiomi4W8mlpKTA09MT/fr1Q5MmTRAbG4t9+/ahdevWrKMphM8//xyLFy/GwoULsW/fPqZZ5GXIe9asWTh58uSbx3CSmnt7RrirqysmTpwIOzs7hIaGMr9QIJLH4/Hg7++Po0ePoqKignUclUIFpQwFBgYCAHx9fdkGIVLx8OFDTJkyBZ06dcLt27dx5MgRREVFwdHRkXU0hbNo0SKMGTMG48ePR1RUFLMc8jDkvXnzZmzYsAG//fYb+vXrxyyHojM3N8eBAwcQHx8PAwMDeHt7o1evXjQjXAkJBAI8efIEZ86cYR1FpVBBKUNCoRDu7u40XKVkysrKsHz5clhaWuLgwYP45ZdfkJKSgiFDhtCkiTri8XjYunUrevTogUGDBuHWrVvMcgDseihPnjyJqVOnYtq0afjqq6+YZFA23bt3fzMjPD8/Hw4ODhg2bBjNCFcitra2sLKyomFvGaOCUkaKiopw7tw5+Pn5sY5CJEQsFmPPnj2wsrLCjz/+iAkTJiA9PR0zZ85EgwYNWMdTeJqamhAKhTA0NISnpyceP34s8wwseyhTUlIgEAjQv39/rF27VubtK7N3Z4RHR0e/mRHO4u8ZkazXs70DAwPx4sUL1nFUBhWUMhIcHAyRSITBgwezjkIk4PVQ9ueffw57e3ukpqZi/fr10NfXZx1Nqejq6iIsLAwlJSXw9fVFWVmZTNtn1UP5+PFjeHt7o1WrVjhw4IBSP4KTpbdnhC9ZsgR///03zQhXEgKBAMXFxTh9+jTrKCqDCkoZEQqFcHZ2VrknoSibtLQ0+Pn5wc3NDQAQERGBgIAAWFpaMk6mvFq3bo3g4GBcvXoVY8aMkWlxx6KHsry8HIMGDUJZWRmCg4PRpEkTmbWtqho1aoR58+YhIyMDEyZMeDMjfPv27TQjXEHZ2NjA2toahw8fZh1FZVBBKQPPnj3DyZMnaXa3AissLMSMGTPQoUMHXLp0CXv27EFcXNybwpJIl4ODA/bt2wehUIh58+bJrF1ZLxvEcRwmTpyIy5cv49ixY2jVqpVM2iUvGRgYYP369W9mhE+aNAl2dnYICQmhGeEKhoa9ZY8KShkICwvDixcv6P5JBfTixQusXbsW5ubm2LFjB3766SfcunULo0aNetN7RWRj0KBBWLt2LVavXo0tW7bIpE1ZD3kvXboU+/btw65du2h1AIbenhH+ySefwMfHB7169UJ8fDzraKQW/P398fTpU5w6dYp1FJVAZ0QZEAqF6NKlC9q0acM6CqkhjuNw5MgRdOjQAd9//z2GDx+OtLQ0zJs3Dw0bNmQdT2VNnz4d33zzDf73v//h+PHjUm9PlkPeBw4cwI8//oilS5dCIBBIvT1Sve7du+PcuXMIDQ1Ffn4+HB0daUa4ArGxsYGNjQ3N9pYRKiilrKysDGFhYTTcrUDi4uLg6uoKf39/WFlZITk5GZs3b4ahoSHraCqPx+Nh/fr18PT0hEAgQFJSktTbA6TfQxkbG4tx48Zh9OjRWLBggVTbIrXD4/Hg6emJpKQk7NixAxcuXED79u0xbdo0mhGuAAQCAY4dO4by8nLWUZQeFZRSdurUKTx//pwKSgWQmZmJkSNHwsnJ6c0wSVhYGGxsbFhHI29RU1PDvn370K5dO3h5eSErK0tqbcmih/LevXvw9fWFvb09tm3bRmuXyik1NTWMHz8et2/fxtKlS7Fr1y6Ym5vj559/phnhcszf3//NPAYiXVRQSplQKESHDh3Qvn171lHIBxQXF2Pu3LmwsrLC+fPnsW3bNly5cgV9+/ZlHY18gLa2NkJCQsDn8+Ht7Y1nz55JpR1p91A+ffoUPj4+0NbWxtGjR2n9UgXQqFEjzJ07982M8CVLlryZES4SiVjHI++wtraGra0tDXvLABWUUlRRUYGgoCDqnZRTIpEImzZtgoWFBTZu3Ig5c+YgLS0NEydOpHX/FICRkRHCwsJw9+5dCAQCqZzMpdlDKRKJMHz4cDx48AAhISH0BC0F83pG+M2bN+Hm5kYzwuWYQCBAUFCQzNexVTVUUErRuXPnUFxcTAWlnOE4DiEhIbC1tcU333wDb2/vNwsba2trs45HaqFjx44QCoU4c+YM/ve//0n8RC7NZYO+/fZbnDp1CocPH4a1tbXE909ko23btti/fz8SEhJgaGgIHx8feHh40IxwOeLv74+SkhKcOHGCdRSlRgWlFAUEBKBt27bo1KkT6yjklatXr6JPnz7w8fGBsbExEhMTsXPnTlpwXoH16dMHW7ZswV9//YU1a9ZIdN/SGvL+448/8Ntvv+H333+nWyuUhL29Pc6ePYuwsDAUFhbC0dERAoEA6enprKOpPCsrK9jZ2dGwt5RRQSklVVVVCAwMxJAhQ+gmezmQnZ2N8ePHo2vXrsjJyUFwcDDOnDmDLl26sI5GJGDChAlYsGAB5syZI9EnY0hjyPvEiROYNm0aZsyYgSlTpkhsv4Q9Ho+Hzz77DFevXsWOHTtw8eJFWFtb04xwOSAQCBAcHEwTqKSICkopiYqKwuPHj2m4m7Hnz59j8eLFaNeuHUJCQvD7778jOTkZ3t7eVOgrmaVLl2LkyJH4/PPPcfHiRYnsU9I9lCkpKRg2bBg+++wz/PLLLxLZJ5E/r2eEp6Wl/WdG+PPnz1nHU0n+/v54/vy5TNavVVVUUEqJUChEy5Yt0b17d9ZRVFJVVRV27NgBS0tLrFy5ElOnTkV6ejq+/vpraGhosI5HpIDH42HHjh1wcHCAr6+vRIYaJdlD+ejRI3h7e6N169bYv38/TfxSAQ0bNnwzI3zixIlvZoRv27aNZoTLmKWlJbp06ULD3lJEBaUUiMViBAQEwM/Pjx7Px8Dp06fRtWtXTJw4Ee7u7rh58yZWrlyJpk2bso5GpKxBgwY4evQo9PT04OnpiYKCgnrtT1I9lOXl5Rg0aBDKysoQHByMJk2a1Gt/RLEYGBhg3bp1uHnzJjw8PPDFF1/Azs4OwcHBNCNchgQCAUJCQqiXWEqo2pGC+Ph45OTk0LO7ZSw1NRVeXl7o168ftLW1ERsbi/3796N169asoxEZ0tfXR1hYGIqKijBo0KB6PSFDEj2UHMdhwoQJuHLlCoKCgmBmZlbnfRHF1rZtW+zbt+/NjPCBAwfCw8MDcXFxrKOpBH9/f5SWliIsLIx1FKVEBaUUCIVCNG/eHD179mQdRSU8evQIX331FTp16oSbN2/i8OHDiI6OhqOjI+tohBFzc3MEBQUhISEBEyZMqHMPoyR6KH/66Sfs378fu3fvhoODQ533Q5THuzPCnZycaEa4DJibm6Nbt2407C0lVFBKGMdxEAqFGDRoEN0jJWVlZWVYsWIFLCwscODAAaxevRqpqakYOnQoTbghcHZ2xj///IP9+/dj0aJFddpHfdeh3L9/PxYvXoxly5bB39+/TvsgyuntGeE7d+5ETEwMrK2tMXXqVDx69Ih1PKUlEAgQGhqKkpIS1lGUD0ck6vLlyxwA7uTJk6yjKK2qqipuz549nJmZGaeurs5Nnz6dy8/PZx2LyKnVq1dzALjt27fXetvS0lIOALdnz55ab3vx4kWuQYMG3JgxYzixWFzr7YlqKS0t5VauXMnp6Ohw2tra3NKlS7mSkhLWsZTOnTt3OADcgQMHWEdROtRDKWFCoRDNmjVDr169WEdRSlFRUXBycsLo0aPRrVs3pKamYv369dDX12cdjcipWbNm4csvv8SXX36J06dP12rbug5537t3D76+vujevTv++usv6jEn1WrYsCHmzJmDO3fu4IsvvsBPP/0ES0tLbN26lWaES1CbNm3QvXt3GvaWAiooJUwoFGLgwIG0NI2EpaWlwc/PD25ubhCLxYiIiEBAQAAsLS1ZRyNyjsfj4ffff0efPn0wdOhQXL9+vcbb1mVSTnFxMby9vdGkSRMcPXoUDRo0qHVmorr09fWxdu1a3Lp1C7169cLkyZPRqVMnBAUF0YxwCREIBAgLC8OzZ89YR1EqVFBK0I0bN3Dz5k1azFyCCgsLMXPmTNjY2ODSpUv4559/EB8fDzc3N9bRiAJRV1fHoUOH0KZNG3h6eiInJ6dG29W2h1IkEmHYsGHIyspCaGgoDAwM6pyZqLY2bdpg7969uHTpEoyMjODr6wt3d3eaES4BQ4cORXl5OUJCQlhHUSpUUEqQUCiEtrY2+vXrxzqKwquoqMC6detgYWGBbdu2YfHixbh16xZGjx5Na3uSOmnSpAlCQkIgFovh4+NTo5vya9tDOXPmTJw5cwZHjhxB+/bt65WXEADo1q0bzpw5g+PHj+PJkydwcnKCv78/0tLSWEdTWK1bt4aDgwMNe0sYnZklSCgUwsvLC1paWqyjKCzu1Sz5Dh06YNasWW+W0pg/fz4aNmzIOh5RcC1btkRoaChu376NESNGoKqq6qOfL62ogkbzNnhQqoaUnGI8f/Hhe9l+//13/P7779i0aRP69Okj6ehEhfF4PAwYMABXrlzB33//jdjYWHTo0AHffPMNzQivI4FAgOPHj+Pp06esoygNHkc3ZUjEnTt3YG5ujkOHDtHyIHUUHx+P7777DtHR0fjss8+wZs0a2NjYsI5FlNDx48fh4+ODr776Chs3bvzXpJm0h8+wN+4+zt96hPuFpXj7AMkDYKbXCL2smmOUoxksDZu82Z+3tzemT5+OtWvXyvaXISqnrKwMGzduxIoVK1BVVYXZs2fj22+/RePGjVlHUxiZmZlo3bo19uzZg1GjRrGOoxSooJSQNWvWYNGiRXj8+DG0tbVZx1EomZmZmDdvHvbv3w9bW1v8+uuv6Nu3L+tYRMlt2bIFU6ZMwbp16zBjxgw8KCzF/KPXEJWeDzU+D1XiDx8aX7/vamGAsTaa8OvnDg8PDxw9epTWnyUyU1BQgJ9//hl//PEH9PX1sXjxYkyYMAHq6uqsoykEZ2dnNG/eHMeOHWMdRSlQQSkhzs7OMDQ0RGBgIOsoCqO4uBgrVqzA+vXroauri2XLlmHcuHF0QiYyM2fOHKxZswbfbz76f+3deVSTZ94+8CsJsqqgKKuASEKi1Jlqf74tdlHftur0VEVtdWY6tYtWO1XbY0GsC2oRBRd0qk4du9dtbM+0bp23VrHaWl+1ti7jYUeWACIYQNlkSfL8/uANBRPWBJ4kXJ9zOG0hPM8XTw3XfT/3975xuMARWr3QZpC8n0wC6Brq4ZL6b1w6+DcOJkkUOTk5WLVqFQ4cOACVSoWNGzdiypQp3K6qHdu2bcM777yDkpISuLu7i12OzeMaSgsoKCjAhQsX2N3dQVqtFrt27YJCocD27dsRHR2NzMxMzJ07l2GSelR8fDzGznsXX+Q6oE6r71SYBACdAAiyPrg3cjo+u1TUTVUSta15R7ifnx+mTZuGJ554AhcuXBC7NKv23HPPob6+HseOHRO7FLvAQGkBhw4dQp8+fTBlyhSxS7FqgiDg3//+N373u99h4cKFeOaZZ5CRkYHY2FjO7JAovvy1AAWeo826hmEWaMuJDHxxSW2Jsoi6pHlHeEVFBcLDw9kR3oaAgACMHTuW3d4WwkfeFjB+/Hi4uLjg22+/FbsUq3Xt2jVERkbi1KlTmDBhAhITEzFq1Cixy6JeLL+sBk9t+wF1WuM9Jit+Pozy7z8y+X0DJy9Gvwcnmfyak4MUSUvGIWCgq0VrJeosnU6Hffv2ISYmBkVFRViwYAFWr14NLy8vsUuzKu+99x6io6NRXFwMDw8PscuxaZyhNFNJSQnOnj2LGTNmiF2KVbp58yZeffVVjBo1CgUFBTh69ChOnTrFMEmiW3HoOrTtPOLu4xWMvg/+ocWHU0DrOw9o9QJWHLpu6VKJOk0mk+Gll15Ceno61q9fj3379iEkJATr1q1DdXW12OVZDcNj76NHj4pdis1joDSToTssIiJC3EKsTHV1NdauXQuFQoFjx45hx44duH79OheKk1XILK7E2SxNu2smnYN+D8/JC1t8OHoOafX1Or2As1kaZJXwSDeyDi4uLoiOjsaNGzcwf/58xMXFQS6X44MPPuAZ4QD8/f3x2GOP8bG3BTBQmumrr77CE088gcGDB4tdilXQ6XT45JNPoFAoEB8fj0WLFiErKwsLFy7k+eZkNfZfVEMm7djARltZCr22vsPXlkkl2HeBaynJunh6eiIxMRHp6el48sknsWDBAowcORJHjhzp9WeEz5o1CydOnEB5ebnYpdg0BkozlJeX49SpU+zu/j9JSUl46KGHMHfuXDzxxBNIS0vDxo0buR0DWZ3T6SUd6uiuvHQYhX9/CflbZkC9bTaqrp9q93t0egGnM3h6CVknw2bev/76K/z9/REREdHrO8JnzpwJrVbL/SjNxEBphmPHjkGr1WL69OlilyKq1NRUPPvss3j66afh6uqK8+fP4+DBgwgODha7NCIjVXVaqMtq2n5RHyfI+g6E2wNPon/4LDgHj4ZQV4PSf29DdfIP7d5DXVrT5jGNRGIbPXo0Tp48iePHjzd1hD/33HPIyMgQu7Qe5+fnh8cff5yPvc3EQGmGr776CuHh4fD39xe7FFGUlJTgjTfewMiRI5GSkoIvv/wS586dwyOPPCJ2aUStyiutRntzk/1H/QFDFu3BoGeXYMC4OfCeHQuvP64DAJQl7W73HgKA3FI2PpB1k0gkmDRpEi5fvozPP/8cP//8M8LCwrBw4UIUFxeLXV6PmjVrFk6ePImysjKxS7FZDJRdVFlZie+++65XdnfX1tYiISEBcrkcBw4cwMaNG5Gamornn3+eDTdk9epNbBPUES5DH4Ss/2Do71V0aE1lV+9D1NNkMhnmzJmD9PR0bNiwAfv374dcLkdsbCyqqqrELq9HzJw5EzqdjqfdmYGBsou+/fZb1NXV9ar1k3q9HgcOHIBSqURMTAxeeeUV3LhxA5GRkXBychK7PKJW6XQ6ZGRk4PDhw9j72addvo7MzQMAoK+paPe1x//nG1y4cAF37tzp8v2IepKLiwuWLl2K7OxsLFiwAOvXr4dCocDu3bvtviPcx8cH48aN42NvM3Bj8y6aPXs2MjMzcfnyZbFL6RE//fQT3n77bVy6dAkRERHYuHEjQkNDxS6LqAWdTofs7GwkJycjJSUFycnJSE5ORlpaGurq6gAAHoO84T73I6ALs+kFO+dAV1WGgOjDkEodWn+hIEC99XkIDbUAAG9vb6hUKgwfPhwqlarpIyAgAFIpx/VknXJzc7Fq1Srs378fKpUKCQkJmDp1qt0+idq1axcWL16M4uJieHp6il2OzWGg7IJ79+5h8ODBWL58OVauXCl2Od0qKysLy5Ytw9dff42HHnoIiYmJGDdunNhlUS+n0+mQk5PTFBgN4TEtLQ21tY0hzsPDA2FhYRgxYgTCwsKaPnx8fDB+yxnktdGYU6/Jh+OggBafq049C82RjZC6eSBg8b426wvydMW3bzyMjIwMpKWltfhIT09vqtHFxQVKpbJFyFSpVAgNDYWLi4uZf0pElnH58mUsW7YMSUlJePTRR7F582aEh4eLXZbFFRcXw8/PD7t378a8efPELsfmMFB2wZEjRxAREYHU1FSoVCqxy+kWZWVliIuLw86dO+Ht7Y34+Hj8+c9/5mwK9SidTofc3Nym4GgIj6mpqU2hzN3dvSksNg+Pvr6+rc6krD2ajL0X81rdOki9bTYkMgc4egVD6jYADbfz0FCSDQAYPHM1XBX/1WrNMqkELz4chLVTTZ+oo9froVarkZqaahQ2S0oatxuSSCQICgoymtFUqVQYPHiw3c4QkXU7ceIEoqOjce3aNcyYMQPx8fF296TqySefhEwmw4kTJ8QuxeYwUHbBnDlz8MsvvyAlJUXsUiyuvr4e77//PmJjY9HQ0IDly5djyZIlnC2hbqXX65GTk9PiMbVhxvHevXsAgP79+7eYaTQESD8/v04HrMziSjz9tx9b/XrJ1xtQm3O56ZE1JBLI+nvB8w+L4TL0wXavn7TkCci9+nWqJqBxIJeenm4UNG/cuAGdTgcAGDBggFHIVKlUGDZsGBwc2ngMT2QBer0e+/fvx8qVK3Hz5k3Mnz8fa9asgbe3t9ilWcTu3buxcOFCFBUV8cCSTmKg7KT6+np4e3tj0aJFWLdundjlWIwgCDh06BCWLVuG7OxszJs3D7GxsXbzJkHWQa/XIzc31yg4pqamtgiO9z+mDgsL61JwbMuLH1/E/2aXdmiD846SSSUYO8wTe+c+bLFrAo3vO1lZWUZBMy0tDZWVjcc89unTBwqFwihoKpVK9O/f36L1ENXW1mLnzp1Yv349tFotoqKiEBkZib59+4pdmllu374NHx8f7Nq1C/Pnzxe7HJvCQNlJ3333HSZPnowrV67gwQcfFLsci7h06RIiIyNx9uxZTJ48GZs3b8YDDzwgdllkw/R6PfLy8ozWOKampqKmpnHtYr9+/UyucfT39++RR7r5ZTV4atsPqLPg9j5ODlIkLRmHgIGuFrtmWwRBQFFRkVHITE1NRUFBQdPr/Pz8TM5qDhkyhI/PySxlZWXYsGEDduzYgQEDBuDdd9/F3LlzbXq2/Omnn4YgCEhKShK7FJvCQNlJ8+fPx6lTp5CVlWXzb8RqtRrLly/HgQMHMHLkSGzZsgUTJ04UuyyyIYb1gKbWOFZXN27s3a9fv6bQ2Dw8WkOYOXhJjXe+vm6x622cMRKzxwRa7HrmqKysNNkUlJGRgfr6xn003dzcTAZNuVwOZ2dnkX8CsiW5ubmIiYnBvn37oFQqkZCQgGnTpon+d7wrPvzwQ7z++usoKiqCl5eX2OXYDAbKTtDpdPD19cXLL7+MTZs2iV1Ol1VUVCA+Ph7btm3DgAEDsG7dOrzyyiuQyWRil0ZWSq/XIz8/v0VwNMw4GoJj3759WwRGw78HBARY9S+VnaczseWE+cfNLZ2oxMIJcgtU1L0MjU7NZzMN/zScEiKVShEcHNwiZBoahLidCrXlypUrWLZsGU6ePImxY8di8+bNGDt2rNhldYpGo4GPjw927tyJ119/XexybAYDZSecOXMGEyZMwPnz523yeEGtVouPPvoIq1evRlVVFaKiohAdHW3za17IcgRBgFqtNrnG0XBihpubm9Fj6hEjRiAwMNCqg2NbDl5SY83RZGj1QqfWVMqkEjhIJYidGmY1M5Pm0Gg0Jtdp5uTkQK9vXBowaNAgk7OaQ4cO5aCUmtzfEb5hwwYolUqxy+qwiRMnQqvV4vvvvxe7FJvBQNkJb775Jr7++muo1Wqb2j5HEAR8++23WLp0KVJTUzFnzhzExcVhyJAhYpdGIhEEoWnGsXl4TElJMQqO94dHe92MO7+sBisOXcfZLA1kUkmbwdLw9cflg7Bh+sgeWzMpltraWmRlZZnc6siwJtbJyampKaj5dkehoaEctPZSho7wVatWobCw0KY6wj/66CMsWLAAhYWF8PHxEbscm8BA2UF6vR6BgYGYMWMGtm/fLnY5HXbt2jVERUUhKSkJ48ePR2JiIkaPHi12WdRDBEFAQUGBUXNMSkpKU3ewq6uryTWOgYGBdhkc25NZXIn9F9U4nVECdWkNmr9BSgAEerpiQqgX/vJIYJe2BrIner0ehYWFJmc1b9682fS6gIAAk7Oabe0VSvajeUd4Q0MDli5davUd4aWlpfD29sb27dvxxhtviF2OTWCg7KALFy4gPDwcZ86csYmTYm7evImYmBh8+umnUCgU2Lx5M6ZMmcI3bzslCAIKCwuNmmNSUlJQUdF47rSrqyuGDx9utMYxKCioVwbHjqiu0yK3tBr1Wj0cHaQY6ukGNyfb7V7tSRUVFSaDZmZmZtO50P369TNao6lSqRASEgJHR0eRfwKytLKyMsTHx2P79u0YMGAA1q5di7lz56JPnz5il2bS5MmTUVtbizNnzohdik1goOygpUuX4vPPP0dRUZFVrxOqrq7Gli1bsGnTJri4uGDt2rVYsGCB1f6Fpc4RBAE3b940ao5pHhxdXFxaBEdDeBw6dCiDI4muoaEBOTk5RtscpaWl4c6dOwAAmUyGkJAQk7OaAwYMEPcHILPl5eU1dYSHhoYiPj4eERERVjfh8cknn2DevHkoLCyEr6+v2OVYPQbKDhAEASEhIXjqqafwwQcfiF2OSTqdDnv27MGqVaug0Wjw1ltvYcWKFfDw8BC7NOoCQ3C8vzkmJSUFd+/eBfBbcLx/jSODI9kiQRBQUlJiclYzLy8Phl9VXl5eRjOaKpWq1y7RsGXW3hFeVlYGb29vbNu2DYsWLRK7HKvHQNkBV65cwejRo3H8+HFMmjRJ7HKMfP/994iMjMTVq1cxe/ZsxMfHIzg4WOyyqAMMG1Obao4xzNY4Ozs3zTg2D4/sqqXeoqamBpmZmS1mM9PS0pCent50pruzszOUSqXRjGZoaChcXe27acrWNe8Inz59OuLj462mI/yZZ55BVVUVfvyx9aNaqREDZQfExMRg586dKC4utqp1PWlpaVi6dCm++eYbhIeHY+vWrTa5nVFvIAgCbt26ZdQck5yc3CI4qlQqozWOwcHBDI5EJhg21jc1q1lcXAwAkEgkCAoKMvn43MvLy+oes/ZWer0eBw4cwMqVK1FYWIjXXnsNa9asEb3D+rPPPsOrr76KgoIC+Pn5iVqLtWOg7IARI0ZgzJgx+Pzzz8UuBUDjWaNr167F7t27ERgYiI0bN+K5557jG6MVEAQBxcXFJtc4lpeXA2jcXqV5cDSEx2HDhjE4EllIeXm5yaB548YN6HQ6AICHh4dRyBw+fDiCg4O57lwktbW1+Pvf/464uDg0NDQgKioKUVFRonWEl5eXw9vbG4mJiVi8eLEoNdgKBsp2pKamYsSIEThy5AimTp0qai21tbV47733sGHDBkgkEqxatQqLFy+Gk5OTqHX1Rob1XqaCo+G0EUdHR6PgGBYWxuBIJKL6+nrcuHHD5Pnnhq20+vTpA7lcbhQ2lUol3N3dRf4JegdDR/iOHTvg4eEhakf4s88+i7t37+Ls2bM9fm9bwkDZjri4OCQkJECj0Yh2tq0gCDh48CCWL1+OwsJC/PWvf8Xq1asxaNAgUerpTZoHx/sbZEwFx+ZrHIcNGwYHB24xQ2QLDOuZTc1q5ufnN73O19fX5JGU1nA2vT1q3hGuUCiQkJDQ4x3he/bswUsvvYSCggL4+/v32H1tDQNlO0aNGoXQ0FB88cUXotz/3LlzePvtt/Hzzz9j2rRp2LRpE0JDQ0Wpxd41n3FsHh5LS0sBNAZHpVJptMYxJCSEwZHIjlVVVSEjI8Nom6OMjAzU19cDaDxZylRTkEKhEG0ywp5cvXoVy5Ytw4kTJzB27Fhs2rQJjz76aI/c+86dO/D29samTZvw1ltv9cg9bREDZRuys7MREhKCL774ArNmzerRe9+4cQPvvPMO/vWvf2H06NFITEzE+PHje7QGe3X79m2jx9TJycnQaDQAGh93NQ+OhvAol8sZHImoiU6nQ25urslZTcP7iUQiQXBwsMkN3PmUqfNOnjyJ6OhoXL16FREREYiPj4dKper2+06dOhWlpaU4d+5ct9/LVjFQtmHLli2IiYnB7du3e2xBcHl5OeLi4rBjxw54e3tjw4YNeOGFF7i/WhdoNBqTaxxv374NoDE4hoaGGq1xDAkJ4YJ8IjKLRqNBenq60fnnOTk50Ov1AABPT0+T3edDhw7l4LUNer0e//znP7Fy5UoUFBT0SEf4vn378OKLL0KtViMgIKDb7mPLGCjbEB4eDm9vbxw+fLjb71VfX49du3YhNjYWdXV1WL58OZYsWcL90zpAo9EYrW9MTk5uCo4ODg5NM47N1zjK5XIGRyLqUbW1tcjKyjI5q1ldXQ2gcXmNQqEw2rxdqVRa9fnXPc3QEb5+/XrU19cjKioKkZGR6Nevn8XvVVFRAS8vL8THx2PJkiUWv749YKBsRUFBAQICArBnzx68+OKL3XYfQRBw+PBhREdHIzs7G3PnzkVsbKzoe29Zo9LSUpPNMSUlJQAag2PzGUdDeFQoFAyORGTVBEFAQUGByaB58+bNptcNGTLE5Kymn59fr20KKi8vbzoj3N3dHWvXrsW8efMs/r4fERGB4uJinD9/3qLXtRcMlK3YsWMH3n77bZSUlHTb2bG//PILIiMj8eOPP2LSpEnYsmULHnjggW65ly0pKysz2Rxj2KjYwcEBCoXCaI2jQqGwqo3niYgsoaKiAunp6UZBMzMzEw0NDQCAfv36mQyacrm817wvqtVqxMTEYO/evVAoFIiPj8f06dMtFrQPHDiAF154Abm5uQgKCrLINe0JA2Urxo8fD2dnZxw/ftzi11ar1VixYgX279+PBx54AFu2bLHKIx27W3l5uck1jrdu3QIAyGQyo+BomHHsLW+QREStaWhoQE5Ojsk9NQ0ncMlkMgwbNsxk2Bw4cKC4P0A3uXbtGpYtW4bvvvsO4eHh2Lx5s0U6wisrKzF48GCsX78ekZGRFqjUvjBQmlBSUgJfX1/84x//wGuvvWax61ZWViIhIQFbt26Fu7s71q1bh1deecXuF1/fuXPHZHAsKioC8FtwbL6+MSwsDKGhoQyORESdJAgCbt++bRQy09LSkJeXB8OvfS8vL5NBMzAw0C4OX0hKSkJ0dDSuXLlisY7wGTNmoLCwEBcvXrRQlfaDgdKEDz/8EK+//jpu3bqFwYMHm309rVaLjz/+GKtXr0ZlZSUiIyMRHR3dLQuHxWQIjvevcWweHOVyuVFzTGhoKE/7ISLqATU1NcjMzDSa1UxPT8e9e/cAAM7OzggNDTXawD00NNTmGkXv7wifN28e1qxZA19f3y5d7+DBg/jTn/6EnJwcDPYdgtzSatRr9XB0kGKopxvcnOx7gqgtDJQmTJ48GXV1dTh9+rRZ1xEEAcePH0dUVBRSUlIwZ84crF+/HkOGDLFQpeK4e/euyTWOhoXjUqm0KTg2D49KpZLBkYjICun1euTn57eYzTR8GNavA0BQUJDJWU1vb2+rbgqqra3F+++/j7i4ONTV1TWdEd7ZiZ1rOcV4+q+x8HnoKVTqHdE8QEkABA50xQSlF154OBAKb/uaNGpPrw+U1XXaFiMMD1kDhg7xxdatW806CP4///kPoqKicPLkSYwfPx6JiYkYPXq0BSvvfnfv3m0RGA3/XlhYCKAxOIaEhBg1xyiVSp4MQURkJ8rLy002BWVlZUGn0wEA3N3dTW7ePmzYMKvaZaO8vBwJCQl477334O7ujjVr1uC1115rt8b8shqsOHQdZ7M0gKAHJK3vDS2TSqDTC3hcPggbpo9EwEDbmtXtql4ZKDOLK7H/ohqn00ugLqtByz8AAQ3lRfjTuN9hwX+P6PQIo6ioCDExMfj0008hl8uxefNmTJkyxapHbhUVFUaPqVNSUlBQUADgt+B4/xpHBkciot6rvr4eN27cMLnVUUVFBYDGXTnkcrnJWU13d3fRaler1Vi9ejX27NkDuVyO+Ph4zJgxw+Tv6oOX1FhzNBlavQCdvuORSSaVwEEqwbtTw/DHMYGWLN8q9apA2XyEYRhBtKazI4zq6mokJiZi06ZNcHZ2xtq1a7FgwQKrGplVVFQgNTXVKDjm5+cDaDwizDDj2Dw8KpVKuLi4iFw9ERHZAkEQcOvWLZNBU61WN73Ox8fHaPN2lUqFIUOG9NjpcPd3hG/atAmPPfZY09d3ns7ElhMZZt8namIoFk1QmH0da9ZrAmV3jTD0ej327t2LFStWQKPR4M0338TKlSvh4eFhweo7p7KyEikpKUazjs2D47Bhw4zWOKpUKgZHIiLqNlVVVcjIyDAKmhkZGairqwMAuLq6QqlUGgVNhULRbb+jmneET5s2DQkJCbha6Yp3vr5usXtsnDESs+14prJXBMruGmGcPn0akZGRuHLlCmbNmoWEhAQEBwebfZ+OqqqqagqNzcOjYQQokUgQHBxstMZRpVLZXKceERHZL51Oh7y8PKNtjtLS0qDRaAD89jvN1OPzQYMGmb20TK/X4+DBg1ixYgWKKuvh/9o/oJe0vn1SVfJplJ/6GPqaO42fkPWBS8gYeM1YYfL1Tg5SJC0ZZ7drKu0+UB68pLb4COP3/WoQHR2NY8eO4ZFHHsHWrVsRHh5usXvcr6qqqsWjakN4zMvLA/DbX7L71zgyOBIRka3TaDQmm4Kys7Oh1+sBAAMHDjQZNIODgzu913NdXR2eWn8E6jpnSKSmA+Wd81/i7g97IOnjDOeg30Pi5Apt+U1A0MP3pW0mv0cmlWDsME/snftw5/4AbIRdB8r8sho8te0H1Gn17b62+OAq1OZeBWR9ELT0UKuvkwo6FH74OvzdnZGQkIDnn3/eYg031dXVJtc45ubmNr3GMOPYPDyqVCq4ublZpAYiIiJbUFdXh6ysLKNtjtLS0lBdXQ0AcHR0hEKhMAqaSqWy1S2DMosr8fTffmz1vtoKDQp3vQKp2wD4v/EJpNLOBdakJU9A7mV/WwrZ9Q6cKw5dh7YD6yXrbmY0hskO0AnAfy3chqSVEV3eU7G6uhppaWlGp8c0D45Dhw5FWFgYZs2a1RQehw8fzuBIREQEwMnJqWlipTlBEFBYWGgUMj/77LOmbe8AwN/f3yhoDh8+HPt+LW+zcbf8zCeAIMBz0iJIpQ7QVt+B1KVvh4KlTCrBvgtqrJ0a1u5rbY3dzlC2N8JoruD9lyHU10IQ9BC0DW3OUBp0ZIRRU1OD1NRUo+aY3NzcpqOvgoKCjNY4Dh8+HH379u1Q7URERNQxFRUVJh+fZ2ZmoqGhAQAw5PWPIPPwafUaBTvnQFdVBo/xL+POj/sAvRYA4DAoAL5/2Qypc9u/v4M8XfFD1ATL/VBWwm5nKPdfVLe7NRAAVPxyFLoKDQZFLEfpt+916Nr3jzBqampazDgaAmROTk6L4DhixAjMnDmzKTwyOBIREfWc/v37Y8yYMRgzZkyLz2u1WuTk5OBqchqWXmx7GZvuXiUA4M6Zz9BncDBcgkfhXs4VNNzOwc1PFmPIG5+2+f3q0hpU12nt7phG+/ppmjmdXtJumNRr61F++lM4eAbATfVohwOlTi/gX+dTceXjFUhOTkZ2dnZTcAwMDERYWBimT5/eIjja27ndRERE9sLBwQEKhQL1bl7AxZ/afrHQ2JfhMCgAfnN3AAAGACj88K/QlubjXs5luAS3fjKeACC3tBphfuJt7N4d7DJQVtVpoS6rafd1pd9sBXQNGDzddIt/WyoFZ9RqBURERDStcRwxYgSDIxERkY2q70ATb+Oxizr0e/APLT7db9QfUJ70AWrSzrUZKDt8Hxtjl4Eyr7Qa7S0MrS8rRE3aT3AJGQPHQQGdvodEIsHWD/fa3QiDiIiot3J0aP+EHmkfZ+h1DegzwK/F5x0G+AIAdDUVFrmPrbG/nwgdS/63v44DpDIMmra0W+9DREREtmGopxva2wjQYWBjkKzXqFt8vkHTeBqdrO/ANr9f8n/3sTd2GSjbS/73ci5Dq8mHi/wR1N/KQa06GbXqZECvBwQBtepkNJQXmX0fIiIish1uTg4IbOckm/4PTQUAVP76TYvPG/677++fbvP7Az1d7a4hB7DTR96GEUZrj70bbjeOKu5lnMO9jHNGXy8+sAx9vIbB79Xtrd7DXkcYREREvdkEpRf2XsxrtbHXLWwc7vy0H9rymyj4+8tw8h+OusJU6Co1cPRXwclH3uq1ZVIJJoR6dVfporLLQGkYYeS10pjjHPL/0N9w9mYzFZeOAHod+j88A44+CuNvbMZeRxhERES92QsPB+Kz87ltvsb3le0o+de7qMtPRk3aWUAqg4viEXjNXNXm9+n0Av7ySKAFq7UedpuI2hphOHoOgeP4l40+X3nlfyBogQEmvtacPY8wiIiIejOFdz88Lh+E/80ubXWWUuroDJ8/x3fquoazvO3x2EXATtdQAo0jjPb2oewqex5hEBER9XYbpo+Eg7S99pzOcZBKsGH6SIte05rYbaA0jDBknfgfInDJl+0euyiTSvC4fJDdjjCIiIh6u4CBrnjXwudtx04NQ0A7DT+2zG4DJcARBhEREXXNH8cEImpiqEWutXSiErPH2PeTTbsOlBxhEBERUVctmqBAwoyRcHKQduqJJ9D4RNPJQYqNM0Zi4YTWO7/thUQwHEJtx3aezsSWExlmX2fpRGWv+J+CiIiIfpNfVoMVh67jbJYGMqmkzR4Nw9cflw/Chukje80kVK8IlABw8JIaa44mQ6sXOtWsI5NK4CCVIHZqmN1PVxMREVHrMosrsf+iGqczSqAurWmx37UEjVsKTgj1wl8eCex1vRa9JlACHGEQERGRZVTXaZFbWo16rR6ODlIM9XTr1ftT96pAacARBhEREZHl9MpA2RxHGERERETm6fWBkoiIiIjMY9fbBhERERFR92OgJCIiIiKzMFASERERkVkYKImIiIjILAyURERERGQWBkoiIiIiMgsDJRERERGZhYGSiIiIiMzCQElEREREZmGgJCIiIiKzMFASERERkVkYKImIiIjILAyURERERGQWBkoiIiIiMgsDJRERERGZhYGSiIiIiMzCQElEREREZmGgJCIiIiKzMFASERERkVkYKImIiIjILAyURERERGQWBkoiIiIiMgsDJRERERGZhYGSiIiIiMzCQElEREREZmGgJCIiIiKzMFASERERkVkYKImIiIjILAyURERERGQWBkoiIiIiMgsDJRERERGZhYGSiIiIiMzCQElEREREZmGgJCIiIiKzMFASERERkVkYKImIiIjILAyURERERGSW/w8oQ4VEp4t0VgAAAABJRU5ErkJggg==", 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" ] @@ -673,6 +523,24 @@ " edge_color=\"r\",\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "e82b5953-122a-4707-8ab6-f741f14f13a5", + "metadata": { + "pycharm": { + "name": "#%% md\n" + }, + "tags": [] + }, + "source": [ + "\n", + "## References\n", + "\n", + "[1]: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n", + "\n", + "[2]: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n" + ] } ], "metadata": { @@ -691,7 +559,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" + }, + "vscode": { + "interpreter": { + "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0" + } } }, "nbformat": 4, diff --git a/applications/optimization/max_clique/max_clique.qmod b/applications/optimization/max_clique/max_clique.qmod index 72f12c9ab..f2779147b 100644 --- a/applications/optimization/max_clique/max_clique.qmod +++ b/applications/optimization/max_clique/max_clique.qmod @@ -1,755 +1,22 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=30.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-6.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-6.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-8.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-8.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-17.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-23.5 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-23.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=16.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=7.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=9.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=9.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=13.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=13.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=-2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-4.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-4.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-8.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=-3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - } -]; - -qfunc main(params_list: real[40], output target: qbit[7]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + x_0: qbit; + x_1: qbit; + x_2: qbit; + x_3: qbit; + x_4: qbit; + x_5: qbit; + x_6: qbit; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=True, -initial_point=[0.0, 0.0683371298405467, 0.0035966910442392997, 0.0647404387963074, 0.007193382088478599, 0.0611437477520681, 0.0107900731327179, 0.057547056707828795, 0.014386764176957199, 0.053950365663589496, 0.017983455221196498, 0.050353674619350204, 0.0215801462654358, 0.0467569835751109, 0.0251768373096751, 0.0431602925308716, 0.028773528353914397, 0.0395636014866323, 0.032370219398153696, 0.035966910442393, 0.035966910442392995, 0.0323702193981537, 0.0395636014866323, 0.028773528353914397, 0.0431602925308716, 0.025176837309675102, 0.04675698357511089, 0.021580146265435807, 0.0503536746193502, 0.0179834552211965, 0.053950365663589496, 0.014386764176957199, 0.057547056707828795, 0.010790073132717903, 0.061143747752068094, 0.007193382088478607, 0.06474043879630739, 0.0035966910442393036, 0.0683371298405467, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=1, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=1 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[40], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 20) { + phase (-((((((((-v.x_0) - v.x_1) - v.x_2) - v.x_3) - v.x_4) - v.x_5) - v.x_6) + (2 * ((((((((2 * v.x_0) * v.x_5) + (v.x_1 * v.x_4)) + (v.x_2 * (v.x_4 + v.x_6))) + (v.x_3 * v.x_6)) + (v.x_4 * ((v.x_1 + v.x_2) + v.x_6))) + (v.x_6 * ((v.x_2 + v.x_3) + v.x_4))) ** 2))), params[i]); + apply_to_all(lambda(q) { + RX(params[20 + i], q); + }, v); + } +} diff --git a/applications/optimization/max_clique/max_clique.synthesis_options.json b/applications/optimization/max_clique/max_clique.synthesis_options.json index 0967ef424..bcb7020f0 100644 --- a/applications/optimization/max_clique/max_clique.synthesis_options.json +++ b/applications/optimization/max_clique/max_clique.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "sx", + "y", + "cy", + "t", + "sdg", + "p", + "cz", + "s", + "id", + "u", + "r", + "rz", + "ry", + "rx", + "u2", + "u1", + "cx", + "tdg", + "sxdg", + "h", + "z", + "x" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 264602322 + } +} diff --git a/applications/optimization/set_partition/set_partition.ipynb b/applications/optimization/set_partition/set_partition.ipynb index 2e21044d2..e5c00499d 100644 --- a/applications/optimization/set_partition/set_partition.ipynb +++ b/applications/optimization/set_partition/set_partition.ipynb @@ -34,12 +34,6 @@ "execution_count": 1, "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:41.367157Z", - "iopub.status.busy": "2024-05-07T16:03:41.366691Z", - "iopub.status.idle": "2024-05-07T16:03:42.091526Z", - "shell.execute_reply": "2024-05-07T16:03:42.090734Z" - }, "tags": [] }, "outputs": [], @@ -47,7 +41,6 @@ "import networkx as nx\n", "import numpy as np\n", "import pyomo.core as pyo\n", - "from IPython.display import Markdown, display\n", "from matplotlib import pyplot as plt" ] }, @@ -66,17 +59,13 @@ "execution_count": 2, "id": "48889b21-557b-481c-80c5-3c0b5c91adb6", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:42.097597Z", - "iopub.status.busy": "2024-05-07T16:03:42.095951Z", - "iopub.status.idle": "2024-05-07T16:03:42.103326Z", - "shell.execute_reply": "2024-05-07T16:03:42.102649Z" - }, "tags": [] }, "outputs": [], "source": [ "# we define a matrix which gets a set of integers s and returns a pyomo model for the partitioning problem\n", + "\n", + "\n", "def partite(s) -> pyo.ConcreteModel:\n", " model = pyo.ConcreteModel()\n", " SetSize = len(s) # the set size\n", @@ -98,12 +87,6 @@ "execution_count": 3, "id": "69359e3e-ed85-4b56-9afb-9dd4b9997ada", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:42.107922Z", - "iopub.status.busy": "2024-05-07T16:03:42.106745Z", - "iopub.status.idle": "2024-05-07T16:03:42.115540Z", - "shell.execute_reply": "2024-05-07T16:03:42.114863Z" - }, "tags": [] }, "outputs": [ @@ -111,7 +94,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "This is my list: [4, 8, 11, 7, 1, 8, 8, 5, 7, 11]\n" + "This is my list: [3, 10, 5, 5, 9, 6, 1, 4, 7, 1]\n" ] } ], @@ -127,12 +110,7 @@ "execution_count": 4, "id": "4a985b32-0670-42f3-ba50-5c0097eb75f5", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:42.121294Z", - "iopub.status.busy": "2024-05-07T16:03:42.120080Z", - "iopub.status.idle": "2024-05-07T16:03:42.128345Z", - "shell.execute_reply": "2024-05-07T16:03:42.127656Z" - }, + "scrolled": true, "tags": [] }, "outputs": [ @@ -162,7 +140,7 @@ "1 Objective Declarations\n", " cost : Size=1, Index=None, Active=True\n", " Key : Active : Sense : Expression\n", - " None : True : minimize : ((2*x[0] - 1)*4 + (2*x[1] - 1)*8 + (2*x[2] - 1)*11 + (2*x[3] - 1)*7 + 2*x[4] - 1 + (2*x[5] - 1)*8 + (2*x[6] - 1)*8 + (2*x[7] - 1)*5 + (2*x[8] - 1)*7 + (2*x[9] - 1)*11)**2\n", + " None : True : minimize : ((2*x[0] - 1)*3 + (2*x[1] - 1)*10 + (2*x[2] - 1)*5 + (2*x[3] - 1)*5 + (2*x[4] - 1)*9 + (2*x[5] - 1)*6 + 2*x[6] - 1 + (2*x[7] - 1)*4 + (2*x[8] - 1)*7 + 2*x[9] - 1)**2\n", "\n", "3 Declarations: x_index x cost\n" ] @@ -174,102 +152,83 @@ }, { "cell_type": "markdown", - "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29", + "id": "0b790906-3951-49e9-b8f7-3e692255563b", "metadata": { "tags": [] }, "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { "cell_type": "code", "execution_count": 5, - "id": "816b468f-a59f-4f2f-8337-4a9d66548425", + "id": "2c26a32e-1035-482c-a5f4-d8dcb428b0a8", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:42.132929Z", - "iopub.status.busy": "2024-05-07T16:03:42.131750Z", - "iopub.status.idle": "2024-05-07T16:03:44.605811Z", - "shell.execute_reply": "2024-05-07T16:03:44.605152Z" - }, "tags": [] }, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "qaoa_config = QAOAConfig(num_layers=3)" - ] - }, - { - "cell_type": "markdown", - "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" + "combi = CombinatorialProblem(\n", + " pyo_model=set_partition_model, num_layers=3, penalty_factor=10\n", + ")\n", + "\n", + "qmod = combi.get_model()" ] }, { "cell_type": "code", "execution_count": 6, - "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:44.608883Z", - "iopub.status.busy": "2024-05-07T16:03:44.608215Z", - "iopub.status.idle": "2024-05-07T16:03:44.611547Z", - "shell.execute_reply": "2024-05-07T16:03:44.610977Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, + "id": "4192e734-85ed-47f3-a8a3-b463f0dadcea", + "metadata": {}, "outputs": [], "source": [ - "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)" + "write_qmod(qmod, \"set_partition\")" ] }, { "cell_type": "markdown", - "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0", + "id": "a5c8e5a9-8fe8-4ca8-a31c-89c9e9b75fc2", "metadata": {}, "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" + "## Synthesizing the QAOA Circuit and Solving the Problem\n", + "\n", + "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" ] }, { "cell_type": "code", "execution_count": 7, - "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c", + "id": "d6bcc87b-7504-41ee-8412-c1161256d0ba", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:44.613782Z", - "iopub.status.busy": "2024-05-07T16:03:44.613487Z", - "iopub.status.idle": "2024-05-07T16:03:44.714090Z", - "shell.execute_reply": "2024-05-07T16:03:44.713444Z" - }, "pycharm": { "name": "#%%\n" }, + "scrolled": true, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/6d0b5153-963f-45d5-84a0-ea52307aa923?version=0.61.0.dev7\n" + ] + } + ], "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=set_partition_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" + "qprog = combi.get_qprog()\n", + "show(qprog)" ] }, { "cell_type": "markdown", - "id": "1fcc3812-c9d0-421c-84bb-38047297b33f", + "id": "34c6ac30-97f4-4cc1-9eb3-c32a79b23c66", "metadata": {}, "source": [ "We also set the quantum backend we want to execute on:" @@ -278,111 +237,43 @@ { "cell_type": "code", "execution_count": 8, - "id": "53bc041f-065c-44d2-b220-dafd9d0504ac", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:44.717234Z", - "iopub.status.busy": "2024-05-07T16:03:44.716619Z", - "iopub.status.idle": "2024-05-07T16:03:44.733981Z", - "shell.execute_reply": "2024-05-07T16:03:44.733405Z" - }, - "tags": [] - }, + "id": "2b2c8aea-0228-41a6-88c7-bddea1e29eba", + "metadata": {}, "outputs": [], "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", + "from classiq.execution import *\n", "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n", ")" ] }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7738571f-3a9f-498f-9a15-9c874f3c3dfd", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:44.736412Z", - "iopub.status.busy": "2024-05-07T16:03:44.735967Z", - "iopub.status.idle": "2024-05-07T16:03:44.761670Z", - "shell.execute_reply": "2024-05-07T16:03:44.761038Z" - } - }, - "outputs": [], - "source": [ - "write_qmod(qmod, \"set_partition\")" - ] - }, { "cell_type": "markdown", - "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6", + "id": "edbf9187-9329-487d-abed-1a3640aa25ad", "metadata": {}, "source": [ - "## Synthesizing the QAOA Circuit and Solving the Problem\n", - "\n", - "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" + "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:44.764221Z", - "iopub.status.busy": "2024-05-07T16:03:44.763821Z", - "iopub.status.idle": "2024-05-07T16:03:49.214351Z", - "shell.execute_reply": "2024-05-07T16:03:49.213612Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/0ff868cf-2ccd-47e7-b5f7-4500845af8c3?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], - "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" - ] - }, - { - "cell_type": "markdown", - "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304", - "metadata": {}, - "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", + "execution_count": 9, + "id": "5dcfa7ce-09e6-41ac-be81-298a281ba051", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:49.217154Z", - "iopub.status.busy": "2024-05-07T16:03:49.216634Z", - "iopub.status.idle": "2024-05-07T16:03:58.972236Z", - "shell.execute_reply": "2024-05-07T16:03:58.971471Z" - }, "tags": [] }, "outputs": [], "source": [ - "result = execute(qprog).result_value()" + "cost_values = []\n", + "optimized_params = combi.optimize(\n", + " execution_preferences, maxiter=60, cost_trace=cost_values, quantile=0.7\n", + ")" ] }, { "cell_type": "markdown", - "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6", + "id": "c2cd6e58-981a-47c8-8fd7-5f2a9bebbc31", "metadata": {}, "source": [ "We can check the convergence of the run:" @@ -390,38 +281,46 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "02454398-b229-403c-824a-b1eb539fbc1f", + "execution_count": 10, + "id": "6542ba4a-9493-4b01-8eea-8202d70b1f2c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:58.977492Z", - "iopub.status.busy": "2024-05-07T16:03:58.976250Z", - "iopub.status.idle": "2024-05-07T16:03:59.020166Z", - "shell.execute_reply": "2024-05-07T16:03:59.019553Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/jpeg": 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CodDCCYC5nrj/9V//BXxCP7GwV2PhJwrJkg4aNpZt/Il+1VVXnX/++XR9SM24RHUA0rz6yle+AsxjIcAiTaXQj6EqYdQ3v/lNiklgUrMo7uo40J4ciKZCtCdxjirHgePlAGgHAiHfiYiSxCNGYKzHyGvz4e3u3bvxRF6jUKLjEswg0EQ8VwADqAAbXvOa1xALYAAMuAGuuII3XIlOmozgEt6UbDRpUB/4IVmQD09SYFYRMEN4HCHBub/5m7/BeMsjKiAJgvrcJ1BBXoAr9AAqADkdCBLBEYZkCUbK0I8/KVtE/MF1AjP3CvhBMaWMlIvswCHCEwBUMzJICmym+IzjYrUmBVR5LO0o4mSNoR6gxdFZAWthIwyEGOLyiOJLdO65AQXx52q2fTCSpHAwk9wZ6IUMqOVKTwVHpgSmvJCEPynjafO28AFHiUvB4TyWCQAYXLdKRN1/5jOfyVu4TYngsM3rxocBe3CXApICDAGVjWD4QIcjSZAb5xwH2pMDDoDbs14cVSfIAZPaJuUNNVG5kN0gAUIZoQ9CAD+kbpgH6lhOQIuBNHIcT1CWiL/7u7/LJGoMs8h30jQIecQjHgFyEJgECckNKEW+hnCkDzYzb8vSJ3HS4Ur64Mfv/d7vMWDMlCUGhgFg1D7UU94mZBgxJ3+lpBCGs6Qgz/oN1odA7X7ve98L1AGWGIR/4zd+AwIMMqGccV/0eAoCqBsAs5SZkVeSYqY0wVj4ZF0W8J7eAP6UjusRSkFhwU5wmrlvMBPmgKNEt84BaZIR6EsiMISscdwTBaYlyVIcy4hYhCcFdHGGltHR6TBBLZ0eeg/o01bdliDpOOc40J4ccADcnvXiqDpBDiCgEe5mvTQwtoQMihD99mhQyj0yGhBC90Lim2oFjiLcgQrCgDcADzDAW/xBAvQt3oIc4ARACyqTCBlxY5OlGYAEofE0DGAGL6gAPVAFbuEwwAJ+PKLjmipsJHGFeBwkYa1FC8RATW/A4Iq3ZA0agT10IPDHSMvcJcLjj74I4kKt6ZRQCxoZbXaFZnIkEa68BXGZdMYgMZ0D7OeM8vKK8qJJ85byUmpekSalJn3ewiKuxkb8KRHB6ILgAGO0f97iYCYkYb6mCNzwFkpwDI3DBziGKkww6KEs0EZxuEFjJiPShBgrhdUFPqAp0+VgJrFIzSoLkiCPkCjB1lcgmJUU9god6igL4eMn99dxoO044MaA265KHEEnwwGDGcQ6iSDlEdzcG0oBGPjgDwwgvsFL7oEu3oJ5JvHxAWMAWiZnPfe5z2WJC+OgYAMYgyg3qDNA2rt3L4GxAyP3EfRolhifyeJLX/qS5Q5aMEUZH94SEizhimMCM8Ou//zP/0zcJz3pSQYb9gpKyJroZPcHf/AHUMukX654gqyE4YaM0JshmBnF+Fh5mXtMOui19AzIjg4BuaO4ExiyKZGV1yjBk4nQQCyTmBiIZdyUiccwCjwjtRe+8IVoq3fccQeJA5wUFqpgAolQBG7I2igBhrkhOhZsBnox/PIIijMWDi4SmCiA64tf/GLA+93vfjdvLQpl4S1l5GqqOcF4RemggWBkYeWlFIz+AsCMWHNPeIihOigUN5BK9VEKohPL+hbcGPZz49AXJjjXzhxwGnA7146j7bg5AMYYVBATNOL605/+FDMpsptHFEeE8hVXXAG0YFZF4qOSGoYh8blHyjPYaYKbRaU33njjBRdcwHxjdE3C/+AHPwCBmDxFaoQhPECF9sY9+QIbrJNh3jLDusygBv9Ybou2CuCREQAJsoJnvCIXJhCBvsAP4EEAEuQKtQA2AAMcosmxDIkE0XQhmEIRhUnX//N//k+QG8M4U66wKrOr10033QScM6CLJdYIA7PBM8gjQQiDPBKEAG6SjJibzWg0a2dBX+CZkEAasf76r/+aeU+8xVqOWomZmjIyRZleC2FQx0FTEuERdZnwlOgjH/kIFnVogwYygmYoAYm5p6QQj//f/u3fwkw4A+dhIAkyvwzGQir4TUhoI30eoRN2kRGswPOv/uqvCIxVnIlmVqEMWpMa4/p0ZZhcTSJMl0PDph5hBV0KjOqsLoO3JOic40Bbc4BW55zjwKnEASQ4xUF8s3bWNCqEPo0QqDApzzxkAqClIdAJA1ImxScYsJQ8ghlMhMZ2ij+qHjDA3CXeou19+tOfJnHmDFtg03S5x7TLuhqisLkHb0EydDj8AVrTxQmJ6kaCjIaap/mb3kZIQMjSRMl+z3veQzpAC0jGMmKM0oAfb+kNoPVipqZEYDxrjsFCsJZXACSrbCEVHx7hBliI0mxrh/CBAK7oqcQFpRiQNgKSfG+55ZbXve51NlLO9TGPecwHP/hBohAATRTLAXjPI6jJFUcpUEMpKcAJ8qFAs0CZjotlZFTBZ9IBVnGsiXrjG99Ir8jiUgpmMpOIJibma6ZhM/GK3pL5MM+LxVHEoo9C7kz/phtkr+gHsAKbhcIUhG4EMIyqzcwse5ukaY/u6jjQbhyQTdWRBc45DpwaHABLcEhqVEYrEV84AAa8AcDAGCiCsCYMj0AIwbgBpbhHggNa3BOAuIbW1mKBFnwAP6ITBccjEGIohaDHx/DG4jKWabANYDDCio5IeAKg3ZLgYx/7WDL6xje+gRJJygkxkEcwHgljWfDY7KDNCDDaTBPFeG5qLiHxIcEk98Tfsk6SIh1TuI05FoswMMpSTkI2R4RFUMjQNZAJqMMu86H4RCQvbABWFuZgY8PH0s4rkuLKK8zFXHGWHf4kbsU09iaZEoBeEawjInTy1sjgyiPEw0bCkCnOikBc/MmdV5YOj7xdknKShbtxHGgHDrgx4HaoBUfDinEANAUCEesogib9kcLgBCOOeAISBpDcIM2BE8Jzj1gHTtC90JWx3AJChkNIeYIh4o0+HsEPpLw9gr52T3TUNUQ/eQEzvDX0RUvGH2WUYDgggVdMy2KmLlOHUKAJj4+lDyXcE4wrsIEn98AtziKSO/5QCG3mT3SzyhpEEZGS4sidwBae0tk4N2+JZZZkUiaYZUfXxFAK+kmZVxSZKxEJnOCZMZMooC/+sIt7IwzGkrL1HoiL3g8nWWBNAF7hYBr3VIHdkB0RDTi5t0cYRZXhT9b4gL5E4Z4ykrWRAYtwhMERxl7RFSAk1BKYiuAeZ/eEsUd3dRxoTw44Dbg968VRdYIcAAmQwgh9i4+kNqGMP55cATAT/QSwGxAIbOARqW2KF9DFvUE1YUwPs3TAHhtbJQypEYa3hLHAbAHBvC3b3IORyP/4j/9gSwpsvKRPggzZsofGf/7nf6L7YubFSIsniGJXw8uEtoQ8KwhXygLkGKiAcxTH7iEJYCOdJCQJQpthZOJpOmvyyA2gxdW6GiRoIJdwzEJCFbjIK2MpI+VAPhExCzN2S6nJiKW6mKwvv/xyTN/Y1THC84qBZPRgEsFcbwMBFI2IXOEkVxKkOATAnA7cNtNvTDD2EgAOQwYBuDE+4wnBXJP+AffNjlIQgFhLmNAcxt07DrSeA3zrzjkOnEocQPLa8CeF4h63pHRAEfpfEoa3PCLuuQG6uAJpS6LwSDrAs8Ftc1wbu7Vc0DVZmcoKJXACyzDHBpj2CRIACYwf0+AxOzN1CB/StEx5dWh2vAL5oIeQZGqENQcDzAiAD7hu/iCZkWE4RylwRrAFsOwYQF2SLyZrC0AuhCcRnGVqdNpbkuVxCSU8QgC7btGfoHQAHiO4NkYLPRZx2Ss0kBqIbm+5p16MMPOBsRQQVhsNVhD4uYQAAqM9c4VmwnO18JaIuzoOtDMHnAbc+j6Qo2DFOUCTM+2qOWVwDkBiEhCiHPULOY4Gibw22ynogieymxvzIQBvSQE1q1k/w4cwvDXdEX0u0eTQ/MgFH0MLUgOJgW3AGJKIRTqm/1kUfEjEqLUrFEIGngRrJj65B2MgO1EEwScCEyUJkNxY4jyCr5Ta1OXkLbQRl6KREQ6C8WlWKIlOlCUFJ7opwRSfV0BvYivmMcmRYKAv3ABQ4aHZyfEkAMVszsXoIWvQ10wL+BArKSCvjHI4g/UCUi2A8Y0ErbIsnSQ1Yh1KeXMYd+840A4ccADcDrXgaFgxDgAqiGlLDixMbprlvkEdr0zQWzAkewKBIEESxlJA0ANR3AM8CZwQBh+g1NAoiQJUIP1tvJMAKG1ovRYXrQ4s5D5xFpdHQ27zJykeueKAHxz4BG6BOs0dCxR3gx9AjpCAFsBDgiQioKpYlYQhBQJokiHpJGBJ3AQRydTSJ5hFJykCGM7xCk96ABTNrkatpWD0GyRDAykk3YJmQLUo0El4WEpSdFBgOGXEEwfrrOyGoMZ2/K3sJEsK5G61TKFsTAEaEiQmjDGBNJNSWL7u6jjQVhxwANxW1eGIWRkOGBiQFoIbKWyJmlhHIouY13W3vAKfkOx4GhDa1XRWwuN4hWsmC08QBblvGGZhEhhLAA+QAELICNjDE1RIIDnJlGRJBKAC20AabiCAKEQ0srnnFa6ZADxxhk/4kzWBiUsWCTLhb2PbeFr0BAUBLRsZNfoNrkhhSTFJgVygB/5Y7iTOPfkaqXa18sIEwlgK9D9InwTpDOGf6LWWCFcIhlorYOLJDQmSeHNhrTqai0AsaEi4vSQA2RlnmhNpzsLdOw60FQdaCcAmEbjSikwiGGto54iq5lbKPS2KhkdTRGpYh53GhicNkuhE5BWtmhsTK5aUCKomUWUt2QQQsZAdNvuGMIeKA0vBXR0HHAccBxwH1gUHlvR025/mVgIwHWSANumrgosgKOAKE7mhL8wNZiVmWCyx2gGxQCYAjBZiXWBjdALJCd8FfhVcwVfQFyBnPIyONr1yDIl02HmLosBb4iax3I3jgOOA44DjQPtwAOltJh8ENfemL3EPOrCbG9iB2sacAyPYZH77EH8ESloJwEuYlZiVYCv8ZfUCDDUMFhRVaxjWPENcG+qD6dQKmjGe4Chq9BIlmKoiIhiPA7bRtpkOw05+bCbAq4Qv9AN4mzy6G8cBxwHHAceB9uEAYhx9CTGOqIcqU6hACpa8c9Q3e6YaqYYX7UP2USlZWDt41KArGyAZkeLGQNQMy/AXB9BadwYNmEcYzVs0V+6tHwToQg9VYjd4mpZMBVA3PIKvCrvRhcAGxoA0W8uyWSAp4wgPZjPmZ12qlS2jS81xwHHAccBx4OQ5AACbiOaG1LjnBkTYtm2bzcO3LEwBS3S5k8931VMAllri4B35CtjqhEnuQVmmb3BjZ7SxhpJ7bAtc3/CGN4CpLK986Utfyva8qL94shUAm+WyyS08YpYp2+HackBe4UjNbpIrOi5AS2DLGuilnvAkQEJDEtjdOA44DjgOOA60MwcQ4OhdiHpEerKgHIJR6tqZ7GbaWrZVG4AK14BDtFgIAmjp1DAligNn2Pkdk4LNw4K57NuOD3YGdhG67rrr3v72t/OK8JxCw566LHtg7/WPfexjbBBvyM35LcQi5SWO9MkLT/IFcXlEe4YMfFCTlwR2j44DjgOOA44DbcIBMMIoMQ2Ye8NdlC4gADFuk+1NtpsxtU0oPzIZLQNgGJogIhqtUYlG+7u/+7ucosredWjD8BErMafTcMQYWMsZZ1/96lfZyY8jWcBgPFGCP//5zxOY01Q4towTV0BfFGWAlsQtzaTmeARoMW5jpsDTjBX4UGdJ4CMzy711HHAccBxwHFh7DiQ6UnJjNIC7YAT36FRckeoJQq89kSeQY8sAGD4Ce8Y1OMjQLDYEziIFSjkwFQXXts8Fa4FhzkbFhxvGblF82W+WuNdccw2734G+vKLkbIbHFvD0hpZUQALA+OOwTmC4oMeU+J8A11oahSEQGQXB0Se0nz0ueV70Kgrh/jgOOA44DpwiHECGo6cxFZfyININTey6XkrY4klYICKcYjgWAGaH+quvvpqN3QFIHoFSUJa5zQzTMsQLYMNZOjhMOucwGZRmHP4E4OAzUtixYwdq8b333svZ6Si1iRJMarxlwRJ5sQaJ7ASH4/PPuTFVeJ1UWMMLDX2D0I9xmCkJKPdioeEtd8BywDUKpx7rpHSOTMcBxwHHgeU5gM0ZqQ4QILSBBu65IShGTXsFBuMDCiwfvy19W6YBwzXM94zCwjvglqlVr371qxnHBW7RdNFTUXbBUYCTbg5MB27hLzzkFUuJ8Cci9yjKTK1Ch7auEHhMGDCVGuJm//79733vey+66CKyo2I4U70ta+HEiRLYXeQUg/E55MWiUO7BccBxwHHAcaDVHGgZAFNwQJErIMoV3GXLXI4uB33x/+lPf/q2t72NG1CWeW4GsQTDTL1nzx7Mzqb+EhcF13azMkP0ZZddZhiMP+GZpM4MarRq3hKYMWM8179bqDXUXR6WnUKG/+FerX8OuBI4DjgOOA6sew4siPI1LgrarQ2eo8uCr9u3b+eEVI5p+/nPf37ttdc+6EEPesUrXoH++vjHPx5F+Vvf+pZptMAqjxwJTixmShMSSMYWAX5z/Cr6NJtsgMdow7Y+mEJxYxoz0L5ux33jysG2LKgqzqB3af1FUCyWGW6XBWaN7S6OA44DjgOOAy3mQMvGgLESg5qY7FFzAUgcQ7kwA6DFn30iMTjjifH5Na95Dcd9M/cKWP2Lv/iLJz/5yRx4jkb7/Oc///zzz3/JS17ynve8B+35LW95y5ve9CYG5MFmpsYxYGwma0A3ubdDaVrM8pPJHkQVDI6gdQFfbQBYXsjorzps0QshzctdHQccBxwHHAfahwMtA2Bw0ZRUIBPEBWtNx4U1mJeBTPDYRnbf//73E4YNN8BsJkiDsoThnjnPzJEGm1GXWXr0O7/zO29961tJCswmgA3IkyaPZusmzd27d7cP60+QkgXUbRro9WO4jRK1CVh61QlZJ5iXi+Y44DjgOOA4sGocaOVe0GixgCLQS+kwLDPWC3wmpmP0XVtbzVszKQO6NvONiLxCezbMBp65ZyYXqZnWy8gxuJsgOrOgwWB8CEYKpMaELG5IOUmE+3XhbHKVoPCC1msKcZM1OnqlAOzj3/RqXRTSEek44DjgOLCYA0j1JUIbSY6tdF3L85ZpwPAWyOTKYDCoaTs5A5nGZSZMJehLmARueYvjkaFf9GPQmpA275x0QGgCUx8Wl5CALs7Ql1c2P4ubdeoAVtNtpe8gWi9P+AHHgb2KHny1Q/PgnOOA44DjgONAu3KglQBsPDHUtHvTX7k3o3Ez00ydpQdknSA6PvY2CZmk04zcFgs1FxgmvF2bk1139wuoKuhrO26KglvhNiVo3Ag9v+7l04Evb5sN1uuurI5gxwHHAceBU5kDzji5zmoX1DUlWLfeQAlmAzbx8FOCt6zo+sb3r/nmd38wX2l4fkbx11XxOqtiR67jgOPA/YQDrdeA7yeMXpFigrTouJFWiwYc7YqFZ8AroHh8zvvqt64K6uWHP/zhuWwu5aWdCrwinHeJOA44DjgOrDgHHACvOEtXPUG1QgO4BseSHXccLAUA13xvz8GxVKNcTaXNPM1oscPgVa8Sl4HjgOOA48Dxc8AB8PHzrI1igLzigGTgFgCu+l6Y6/LCDLO9xTYdeikHv8Yjd3UccBxwHGgzDrgBwjarkGMlR+3PsQmaSIbB00XPz+ZT2c7ZUigacHwkw7Gm6sI5DjgOOA44DqwVBxwArxWnVygftT83pyVKsBmjuU7OetUwqHvB9HwZAI4U5Obg7t5xwHHAccBxoD044AC4HeoBdRYyjn6ltrAoR0ZlHjjxyWcQIWCglz9cS5VqyUvNB7m5WihrhWtuD452qF9Hg+OA44DjwDIccGPAyzBlbb1QU01TPfrV9wIqDLAOFbFZaISPF/qphtfN6K/vDY+P1fMdU7XqXCOV87wci5TCdIzYa1ssl5vjgOOA44DjwBE54DTgI7JnbV4em/prKjJbXqEBg9XYmase1mZ23wh4Zl8ssJnNv6peqhJkMUSLBiwDwYbra1MSl4vjgOOA44DjwLFywGnAx8qpVQwn2zUfs9NBYCIswtV4qnO5WmlwH/psiy1m6VBDH3PaLqDjgOOA44DjwJpx4HhE/5oR5TI6Zg4sAdhSqWJnHjMYvAihjzlBF9BxwHHAccBxYG044DTgteHzSuayfKdJt36eL5WwRjM5i6MpNMtYNV7J/F1ajgOOA44DjgMrwIHlhfkKJOySWHMOoA2XSmU0YHTfcqUmyrEzQa95LbgMHQccBxwHjpEDTgM+RkatXrAV6gPpxGgZA+YGDbiuAOwOA169enMpOw44DjgOnBwHVkj6nxwRLvZJcSA2MwO9wK6MAYcB8KtjwK5+T4q1LrLjgOOA48DqccAJ6NXj7TGlbIO1lUqFQ4uJoLOXPbtfNr7PMiQxLhNYEVYCaSUqDI+Pj2dyhTqLkur6mp06nHMccBxwHHAcaEsOOAHd4mrJZDLlcjlQh/KaTsugQKnEwb7LOaA31ncPfQ0gV2qsBA7B72q9jkLsHdcCp0NTdD6OA44DjgOOA6vGAQfAq8baY0sYlGTCVCqd5qZUrnLll0pn7WbpNUZf/h5ac2i95XK13vDA3hIqNYHi8MdGiwvlOOA44DjgOLB2HDhUjK9d3i4n44BpvdzLBGY2hA69bPZEJsex61WxzBkMIXtSlqtsxOEA2Bjsro4DjgOOA+3IAQfALa4VEDebzWI35lco5Bi0rRp2nhBdlUo1CNKBn2Ys2Wm/J8RCF8lxwHHAcWCNOOAAeI0YfbhsQNxUymcSFlfCVCq1E1F/FWzBcpYh+b7vpYJKVY4DFhO0w+HDsd75Ow44DjgOtJQDDoBbyn7NnMlTqWyO60+v+/k3vvVtOz+Bx0N/y9PKQLFMn+YkBrTnqgcC+yluiG5nJi0fy/k6DjgOOA44DrSUAw6AW8p+gLNeY1lROuXN1rxPfek7H//8N8amyzKBeTkHpiZv0GxTXiDHEVKHDPqGHjO4ZBVTGHJiUoPp0EB4Enq51Jyf44DjgOOA40ALOeAAuIXMJ+tG2q8HvuxaVUt7N810jHefs2tkGisyPoae1FDyI0Jdz/3V1xw4CApzIqFX8T0/5+06MN3V0d2Yr+R8v1YqE6vBWYWtLZ/L3XHAccBxwHHgMBw4kdm2h0nKeZ8IB3zZVkN2by563kyqN/DCEuf7qv1Z/qizYdxFUCphiBSgAnPqAjOegeFqTfbBSgcpv96Qc4GdcxxwHHAccBxoYw4kQr6Nabx/kMaYLfDJ+G3Fhm+Pv9Rs30EKzKlmIw5mdR1/Ai6G44DjgOOA48DaccAB8NrxevmcMDezcyS7cFQEgAlz2G2wlo+/4Ds3N0cKHR0dXNlda+GFu3MccBxwHHAcaD8OOABueZ1EVVAu6RbQQYrdrJalScZ7l30RewLAKNDd3d14OA045or76zjgOOA40KYcOLJIb1OiTzGyQi8QDbga1hqyhuhkNGC2lAaAudqhDqcYo1xxHAccBxwHTiUOOABueW36snkkU6iqdTlI0E+xh4bMy9JJVjFxADS/ozg71KGzsxMA5kyGo4R2rx0HHAccBxwHWsoBB8AtZb9mbhpwjVOMdAy4yqEKx+8AbNl+0veZhAUA23Dy8SfjYjgOOA44DjgOrBEHHACvEaMPk03A6UWc8MvbianJjq7uYqksc6kOE/oI3iQxOzvL0O/mzZu5ksgRArtXjgOOA44DjgMt54AD4FZWgZiaA1kIjH0Zw3OdY4yCVI0DBU/Isf0kGnA+n0+xr7RMrvbEqO2c44DjgOOA40BbcsABcKurRbfdACc5BMnsxuivILBg8xLSTFNe4hk/EoXZWxifu7o43SHFDXOpnSE6Zo/76zjgOOA40HYccADc4ipBVTWs5SAjJl8xCatYWX4Z0lEJtenTnZ0SkGTZigMYPmosF8BxwHHAccBxoCUccAK6JWxfyDT0pQrA4DLrkI68DEm26zisw+JcLLKdpQcAx5r0UdYNHzYt98JxwHHAccBxYPU54AB49Xl8xBwATnCVH4uIamEDzRUkbo4hY7lqpm72XPYeDZjoTRrwkQB72RScp+OA44DjgOPAmnHAAfCasXr5jJKB3jLrkGwv6GW3cU7CLZ+MHMZgANyVi8aA3WZYh2GV83YccBxwHGgLDjgAbnE1pHSDSXTeap3RYNFZOdmX041w9kpvuUQ1JQoxjj9iu5bw/AGd5xpesRZkvbDX87IpP/D9aiM8kQXFmry7OA44DjgOOA6sNgdaCcCoaMmOidzjrLS2htVGNAnQvKmTeXLaT8KX+fl5u7eBT+JaGDyTuLYxBat02m1WsC8n9lZt06rJYiMLbNZrY3OhHaQAAMdnFWoRMUSHgsqCwT5/+XEvrACw90x7Qe/WQqPY7Xk9mTCEbWEKYF7glKbhLo4DjgOOA44DbcKBVgIwezYxZjk2Ngb0co+bmZkBbtlMEbAsFApc0+k0i2pgFmHAWjy5t8m9w8PD3HP4z759+7ghKfCVuIQxDM7lchaX4VUwO5PJdLFGp70cZxCCwWJALtcFbvnVvLRpwIK+CYBGmm9CfaAasDzyBpSdqXrVMNuR9mBQRyrN62pdjgp2znHAccBxwHGgPTnQSgBm5ybgdnBwEOh93/ved8kll5x33nlbt27duXPnT37yE/gFZF5xxRUgKw7s3LZt23Oe85zJyUljJdFf8IIXEOYxj3nMq171KjwBbK7j4+NgMCkn6jXpGxID9ha3ra62VoheAsWkD8GGlACwaK5LQfdIVDMDmiLTHSEQ5eVKl4VNtpxzHHAccBxwHGhPDqRbRRZIA6aaMRlMBX3f8Y53XHjhhfh/8pOffOELX/jlL3+ZR+D5xS9+8Uc+8pH9+/ezyWJPTw8EEwtt+OlPfzpo/aMf/QhV+GUve1lfX99b3/pW3qIEcwVxASQrHcDGDfDcbLu2V62/hqEBMFq7ADAkNhonoLmWSrL6yAAYswH3lFfL3foiOgocBxwHHAccBw7lQCs14IQasPMJT3jCU5/6VDTgc845ByQeGRm54YYbgJO9e/eedtppbK947rnnTkxMWBSUxZ/+9Kf33HPPJz7xiYc//OHPeMYzXv/613/sYx8DdQiD5RnoxZlCTBR7RFfesmVLkmm73KDyKimMXvupgBMZoDYeDY9pPAZV2E4Rts4HJnpB8WpVbPfOOQ44DjgOOA60JQdaBsCmlQIV4ATYA+qAsrAIzfUzn/kMkPzIRz6SRzD4Xe96F0ZptOHPfvazNrhLyGuuuebiiy/GH1UYU/OznvUsMHv37t02ysuCHNKxBC1NMsJzaGio7Woh3q450oDBY8HOpWSKLfmIMEx0+h8GwIkGvDQV9+w44DjgOOA40DYcaBkAAximmDJUCWyAl9icBwYGAObXvva1YPAFF1zAKO+LXvSi66677itf+cqb3/zm97///b/1W78F69CA7733XoNwEJq4DA+D2XfccQdq7ujoKAmSrDEZhGboF5DmFRDVNpxfRAhklSrRGDBKMPOn8IlAtxl3m+8XJSDbYIHcZoK2MeDEALA4oHtyHHAccBxwHGgLDrRsDNjmKnMFLJn8zPWZz3zmtddeizaMpvu85z3v+uuvxyL9yle+EiDBqvygBz3o9NNPf+xjH4vl+ayzzgJ9GRIGcsBXtF4gh4FkA54NGzYkrMXzve997wc/+EFuzDCbvGqXm7hTIFPGtDuER9Myq2MlU6Zc6UxvIqABwx/p3xxrbBfOccBxwHHAcWCtOdAyDZiCgr5cgYnu7m7QEb327LPPvvTSS//6r//68ssvZySYtzE8CV94xfXuu+/mysAwNmewGfRFwWWKVn9/P+lIOM8DlQFjIA0fZmbt2bMHZZrhYWzUFqB9rpxAaG6+XGIwuKOrExxlRlWzOxa1nbnfqL8y/u158AQABpLdGHAzG92944DjgONAW3GglQAMIwAbsxVzBTzMajo1NYVOjDmaR+AZfwAVBL3tttuIgrWZ60Me8hBM06h609PTzJT+/Oc/D+KiGfOKpMwSC6hjncbybHo2M6h7e3sJ0FbOT4sRAsCFFTZuDXbSF1lCJNh8ZBimt0FnhcISkYJzdQrwEh66R8cBxwHHgbbiQMsAGHAFcoAK0BGz8xvf+MZvfOMbDN+yAhgN+Pbbb3/c4x6HVscMrKuuuurgwYNXXnnl7//+7z/qUY/CEA0HsUU/+tGPRlE+cODAl770pY9+9KO//uu/bpOczRANGoFApkADacZ0ALutuG/EoLPa+QuQhym+4XtF1YYB3SXuCBgMZlNY5p1RVNLhXmzazjkOOA44DjgOtCsHWgZI6GrgK1esxKAO9mSWEt11110or095ylPYl4Nh4Jtvvpnx4H/6p39CzcWsCsRilwZdgBbU2U9/+tNveMMbGBtGRWatMAO9MBntGUQ3DObRAJjwhkmmbbdhXTDtmV4CGjDFn9VZZsdLpE3CEuOzbmBCdDMnHG86LrzjgOOA44DjwNpwoGUADBZiZKaQqG4gKCos8AMIsVgIEAI40V/ZnQMMJgyasU2twjRtI8dMqmLQ99///d+JQmDTcQFajMykgMmaRMweS3RSRtVG4bZga8PZY82Fidm+V2t4KbTzwM/kcxRnCXYeqgofmjhFJiIAzCsKzv2hduxDYzkfxwHHAccBx4FWcaBlJuhESQVsGN/lEcwAJgFOoBekTMASpdbQFz3YplkxnYpFR7YrFsBDSBLBFm24hRmWRGAoiGtstaRIHCRuFaMPm6+ujEIDhjwc3YtlsfMIxmdL2TTgzk4xQRsAL0HxwxLgXjgOOA44DjgOtIIDLQNg9FTKC3yitJneZjCZjFwm5uJkKNdgFcO17bCBEkwUQIsAQA7bUoJeJAjugmEknkRMkNj8W8Hnw+cZBNBaKkFaiJ0cazyFgjl4Jr/DR154Y2PAOq88GgN2ALzAHXfnOOA44DjQfhxoGQADNnADuAUnwB6AE/UUFMHf9FRDWbDTdudgohavCMCjWaENjy0YSRn04okuaFhOeLsBpI3z7QfAEBbIJKxKlcW/nUG9J8sZSOnZSjTnGXWWH4G4LnLSwTAn0flVWPQb1vMpj2nQ6aDKMYX1UPwXAsYR3F/HAccBxwHHgXbgQIsBGDMyyiswaWjKYPASpiTYiWrIq+YAiRHbAJsULLCFJHCi+C5Js50eA+zugOvUTDUVNjbnqpvy4Xy5MVvPg53iRAtugMmJ6bzCLllMsCrNe/UaJxKWG17R8+b4lcpBWNna7WF870lVApZ3BSleReloYu7iOOA44DjgONA+HGgZALcPC1pLSeiLCRr1NeX5aMAFn2OBM6V6Khq+FgCOlFg9HpjJ0oLFzDwzslmzRMiiqLqMcPuov5xEmJFE6rW6HGvonOOA44DjgONAe3KgZbOg25MdraLKBrzR6TGbo8cfdQKzmNZ1cbOtcGY8HZ9sJmv9KTMqYNtfarhuVfFcvo4DjgOOA44Dh3DAacCHsKQVHiAuCMp4NtiJRZ1h7MNRYYgrxnYDYA03hw063toT1dgA+KgofrgsnL/jgOOA44DjwBpwwAHwGjD5SFmgyfKaac8AMJPLUIIBYCaUHSkOJmibVsYCaA03NydbV9rcNCzSJEJqAupHTsW9dRxwHHAccBxoHQccALeO93HO6KwGwGjAYCfa7RE04CjS4t2emSKOfzxvPAJgzNoOgGMeu7+OA44DjgNtxwEHwG1RJQKWDOIewxiwmaCNaFtVBcoafhMdf3YfYSCZG7cOuC2q1hHhOOA44DhwGA44AD4MY9bQGw2YPUPI0GZggcTJbiTLUpGsZk5uDGsNd/EMAtKQ84CXje48HQccBxwHHAfagQNuFnTra4EdMxj0BXQxPm/a5GF/7pTtOLwG/w9xaMCAa1jVwwezOTu2EBM0cMteYARnWTX7YQHDRx1IPiRt5+E44DjgOOA4sHYccAC8drw+XE5gqqm82JBts2p7FGvzchhMOmBwc2rNGjAv4o2/moO4e8cBxwHHAceB9uKAA+DW1weQyYxldFadBS30MCQM8i5grNiaF0NujMAWxsaAk23CQHFiEOcw8N36IjsKHAccBxwHHAfcGHDrvwFA1KY9cyhFlg2x9HCnReO38bFO0BrhcLwOWHzYh1JnQdsyJB4zbIglO2iFVfnrnOOA44DjgONAO3LAAXDra4U6QIUFLzs6ZPoyI8GCndVYf423okwIFd0W16QSA8DAtu2nzZsEgGuLYDxJwN04DjgOOA44DrSeAw6AW18HUGAAnM/LoQsAMK5SkQ2ixSnQGtoa8iYAzAFKGkI0YAA40YDTvpmgBcWdcxxwHHAccBxoTw44AG5xvdQbArPMogJWdfmux05YOACYc40j4mJldxEANx33BH4TMpeLRvTtDwm6hUgtrl2XveOA44DjwOE54CZhHZ43a/ImbKTCwCtxfG/QyAcexzFm/UaYCip1ryoTsQI57cgAmAVIzMWCKoA5JT2nhurCaLnlaiPwqnnd1pIAokaHXo2lwI3Yjr0mZXGZOA44DrSaA0iHoBKvn0BMMCFEhMYiZ5YzOYftkFeLwrmH1eaA04BXm8NHST+dAn29Wb82Wy9u6fLSNW9rT0etOD00PQ4UlwFhP2hw6CBwW68Gfs0Lqxw7WGkENQHZNOjLb3RyJqjOnbU5QmhCb9rQV677c3WBb+ccBxwH7h8coMddL9fCGc+7Ydi7dUxOCteBrEgMRCa1sM4p49zjG/ncP7jThqV0ANzKSrGvv4qyKz1VH3NENvQ6Aj/lNypqPw7RfePVREFIe5GWoyqwtCtwmRT0JsiE9azvMfpLStqrpYGl+e8aWCsr2OXtOLCmHGjUZmcyaX+o6L3rQ59694c+OdMQlJ2bnW4SBGYWEznihMOaVs5ymTkT9HJcWVu/sm5nZTs5A7gcJkj+treG2Z6jedDyEDUZViHRfmTLSRYN48v4sQwcLxiUmJOFZzxJa23L43JzHHAcaBEH0p2d0iNveAcOHMinVAIEXmdXVyw5WkSWy/YwHHAAfBjGrKG3zqBiChXjv7KJlR1qZHtxyJ5Wh3ZT41MIDZ6LJQFgYNvmcJEIqAwag8G12hoWw2XlOOA40GIOcEx4fXJ6vhp2dPX0AcBIgLlKLduBf4spc9kvywEHwMuyZU09i8UyYGm4awDMo+yNpXgsVmdRgZsakGrE8lb9ODuYANlsPAcafz3XgTIwldo5xwHHgfsPB8JqPZ3Np2re5OQkAOynOJw0XauW05ncoT35+w9b2rakyGrnWswBW0RkAAykogoDwGjAGJkXORTeyBgt3jKJQmG5WBQAJpbVpWE1Bm1FcYfAwiXnHAfuJxzwM5l8PgiZDpLJpTNyPmk64F73xrufsGBdFdMBcOurC6yFCBv65cYGg2VlcEyaasDxg4748pCM75ZKso+0WbCJUtcXpGYovhDN3TkOOA6c8hwIvWLZGx71soUCffqRMa/GPKxEWDQVH9HfZFVreuFu15ADzgS9hsw+TFZ29pHMoVJnNxwvmADwsvESZVhCyiYeUpWyMlhfJIksG9d5Og44DpyaHGAwK+chO0rlapCiN86eAmwmkD6yMDk1WbEeSuU04NbXEsO9YHBvby8IjGGZIxmAT4ZwsB9ZzzWb1WoCWXXalZ9KEUzmZ+kapNnZWW6IzhWtl20suTE8JmXunXMccBy4f3CAyVYBs6AZ1cIkhu4rKxrvHyVfp6V0ANz6ikOFhYhmDRgt1jwPR1yMxfIeYzW4a4ib2JQMhpc1PR0uTefvOOA4cApwAGW3ilFMNuoJ2AtvmfW+DpTbppodALe+KkxPtTFgVFy7sTFgU3MjEm3VUUyvPYG4dpRhchKDvTc8tsXEcQz313HAceCU54CI9Go1WoBYrS4owAt3wgPdXe+UZ0bbF9ABcOurKD5KQdYJAKg2Cat5FrSO6jbRqdib1BwmaDTgzs7OphCSCGq0A+Bmnrh7x4H7AwcA2jJrKNjCltWM1WgdxGL0XWBDYjNb8HJ3a8iBRIyvYZ4uq8UcQIUFQW0ZEm8YvAE7bR3w4oALTwkk034MgBk5bm5jCYovxHF3jgOOA6c6B2yyVaUSacBuFkibV7gD4NZXULMGDDW5nKzeW0YDXmyCJoxpzIkG3AzA2LFBcVvg1PoSOgocBxwH1ooDyIFSpcxSYBxI3CwW1ooEl8+xcsAB8LFyavXCGUyazkouLJo367F1Zo+arynQjAFbSyOuJiJL722B01FTcAEcBxwHTiUO1Bj6BYHDwAahHAa3beW6dcCtrBoaRs33So0w0/AKusaIDlE25bGJeqUuRw0KltKTlVONOOyXl9zIlGk7GBhvplLMNcI6s6CzdVYxEZZXaNB5wvlBueHmO7ayfl3ejgNrzAGRAEzCatTrfjoM0lUOG9e9afHEaZ8eH/HERX/swV1bwQFXBa3gepwnWMoa3rmqlynVN+dzKK2A6MZuL9WoDI3KREYdyQmDkGOACRvUgGs5RszLEY4DgWlpnndgptgoZLo6ww4sTqVKLgi6WBac9sJ0dqruADjmtfvrOHDqc4BVRyUOdSnVGhU/Vw4KpTq7QdMpl3PEPa66c4DnZ1ggzL2h8qnPlTYuoQPgVlYOHVLaBJs+p0LRgKWpAK4BvdawFmYBV7UdsZyev3KKJwv7RE9GyW3IKj8csFwKffaizGYEvH18Q/Dby/k1usCVkFvnHAccB+4fHKB3HtJJZz9abGPpupeqNkzCIxaQNGI8Q2zEevD9gyftXUoHwK2vH5upmIwBp9PMifbZQ2MBO+PpV/jLDlj6xvxoUgzz4Aq5PCWx/Te4sW09KjVA3DnHAceB+w8HRKQjELiGDEK5E0nbu+YdALe4fuiuJqchac+UJbyCo+Av4Co+ovJG5yAJAOOYZxXtSik7rdPYCNzR0cG7lIwTi7Yss6D9qB2Kj3OOA44D9w8OIDRkDYXKisXrIIKFPv39gxXtX0oHwK2vo3lO9GXaVD6zBIDpvDLNSpzhbvzX5jmbHz1dNq0Edztygs2iAWNtashGHKD14uanSbmL44DjwCnMAZ8BKjmJwbaiLJXLCro2hfMULvZ6LZoD4NbXXKwBy2m/NB5mQUcacH3hzEF5FwMwLcrQF1N0ldMW6g3glglcdWxOvFAlmd08HAC3vmodBY4Da8kBDGBsgAUAx1tRJjtyrCUVLq9j54AD4GPn1WqFjMeAo/SpErBTTNDNALwkc0VZAWDmNoZhPiszqEOM1qYxcxoSW1GiAddl3qNzjgOOA/crDsiwlCdbUTILRHvuy8p5kFrV4/sVa9qssMtWTJvReKqTYwcf6Xm+EYACwDLOi0IcA+pSHigAS4C6zIm28xuap23ZYQy1ox0qvDRZ9+w44DiwvjkgyyPq0diVJxNJnGtjDrQMgM3uals1oQJ+7GMfe+ADH8ihtmyJfMUVV3zlK1+xQ34mJibe+MY39vT0sNPTs5/97PHx8bm5OePnHXfcgQ97IA8MDLz+9a83z5mZGbth+NNmAgNLyVCoYFqbuZKgbAgf+nJeoLAKgX19fSDoxIQnu1LqYYXJMDDBU4rVePN3YmKK3u6GgQEqspCWPSxx7M7R09fLdnTGZPMUQFdnj+7qOOA4cApyoCHYi0Rl8SILG7GB8SjGtCZn5rMmD3fbMg60DIABWnDRoJEByzPOOOMNb3jDNddcc+uttz7gAQ/47d/+7d27d4Odb3rTmz7wgQ988YtfvOqqq4CTZzzjGSDx9PQ0DPv1X/91tkH+0Y9+9KlPfeqTn/zk6173Ojy7u7tBGW4YFjXNkvHU5JwDe9UyZi+XsTWN5DBgg2B7ZBKWPNp4r14xGDX3IPADfVNsgxXIsvpmhw5tanTiySP3dk083Y3jgOPAKcUB2QrAE9RVF5nAZIuBlon6U4q9K12YVtYKGMniGR3srKP1vuQlLzn//PNPP/30D37wg2DtLbfccvDgwQ9/+MOf+MQnePvwhz/8T//0T6+//noUX2J97Wtfu/POOz/0oQ+de+65T3/609/61rd+/OMfN8SdmpqKP756shkyuaBS22jrSvPwpNLTBXuRDZmEDIDhDPeVSlO/1WBYs2Kxfdy+vNJ8kSnQhazMgba4QDRhZRoX8LzYAuXQ96SqykV2HGh3DsgkLIRDpVpnFjTDwIkAVMIjCREVQvfUa/cCner0tRKA7eMAKgBOwwYAkpt/+7d/4woSA7GbN29GIbZaeMpTnrJjx47Pfe5zmGe//vWvX3TRRQC2ge7LX/5ycPeGG24gJPZbrii7pIwqSQDgnHtUZ1LjVVu5UknIwQbANYFVSOXRjPALxufktcRgo3UZ/cUGYEVTP3sB+rIRByz0KTuR2lDvX6DW3TkOOA6sKAewk5XlECTgNihXmI11BCf76znXQg60DIAxPttEIYZ1UfhAke9+97uos2DPa17zGjD44osvvueee7A2g7LGICAWCzM+IDemVwaMWUHLGDBvDxw4MDg4SHgLCeICP6TPlcDgEBow0VGgW8jrZbMulVhIpGcAx69pEWYzXwBgtVOb2ktzam5RjHnTyYBp0triFGh6AbOiA7+qI0AOgBPGuBvHgVObA7JbrYwBV+UwJF8Gg0/t8q730i0I7TUuCaALTJIpU6hMSX3wgx985ZVXMqb7h3/4hy996UuHhoZQWAGhyclJgnED1gI2XEFWgIcbbNEgMW/RjIFY3nLPLC0AzL480P2d73znli1beLVx40aM1QRoK2d0mgZshIGvizRgVFjde3LB7qzhzCaNBmwAvKQjq5yQ4wib0bqtCu6IcRxwHFhxDqBykGapWrEOud5IXzwZoVrxHF2CJ8OBlgEwGnCCOuAEkInpGGX3vPPOe8c73nHZZZcxrIs/ei1rbEApAoA0u3btsiU327dvZzAY+OGRYGNjY8yX3rlzJ7zo7OzkikrNFXR/85vfvG/fPlIAjL///e+fDLNWI65NQ6MUaiuWHIDMeAyYVqQ+CsB2vwRo6ZcwBpzLRPOfJYwCNaWnKdIpUY8lkSwld3UccBw4BTlAaze1hLIlN6dgOU+JIrUMgMEYPg7wAzaCxNyjEAOl6LX23QC3zL0CWRnuJQBwcuONN6L4PvGJT8QK/fznP//2229HFQZjuDJN+swzz7zkkktMqyaY4TRJcYNCSXakjKLcVrVGUyk1gkaQyQUeg8BMnxBLMgf6pmqcYlJq6PEldF91VV/I2mDpxyq2ajHKgVeshym/nk/psZ9NZaMfzAgxq4SZx6Wb0rmxnibuuFvHgVOXA0iJRkPOPgr1SBcexRgtpulE2tMv58e2te48+BZ/B0mVrDUdACegmMDkW97yFsaAgVsmP7Oo99prr33Ws57F2z//8z//rd/6rZ/85Ce33XYbM63QjHGYrB+i7mlPexoTr9BrUXOf85zngNM4TNAMFVMeglmpwHK7SXzWurSHyQ/r+fB8o5bpGMimoThPi6h7nb5XyJXrWX+kWKYZ1SpekO3wylU/Jbu8+vVU0Eh55Xl2nWNB9BA2prmpHX0dlFAKWQvrjSoozYHBnGGY9lNTM3JEA+nQ4IhOF0dpsRZoV/Hglf30rbs4DjgOrEsO0Fefr3n06CvhbCbbqNbYaEC69TJpMwJgJACCh+55y4T/uuTs6hDdsh4QNmEUUzMUU7TR0VGw9uabbwY7H/3oR//jP/4jAIz/X/zFX4Caj3vc41CRn/vc57IqiYlX/f39vProRz/6t3/7tw996EPPOussQPrd7343ngTGBI1p2jRpfNrZ0TaKjYAzs4FejMicCkzD8L16JlOvByGn/IqH+FkbEhBVXVcbFFMtiB76qbBWAGktDIYCHQQiBKZpZoJzMijdEJqaJSVd4Tg1SzvqC+sffKKuSvzO/XUccBxYLxywlo3eIf84tjRkMjRYa3LDZAgihFBgsEr+paJgvRT01KGzZQBs84yAYYzGAOr73vc+ZlQZeDAsajcM6/IKZGVUGNUWcE10WQZ00YHZguOzn/2sTdeiTvBk0JebdYG+9hHZGLAN+iafFYXlHuZwtclW9keAV7uz8ledjfEQPg5Gu5NQ/McyX601TOPViFEU98dxwHHgFOaAnKLWYB9oMUDXwmRPjlO4xOu4aC2zQrBwCEAFhm3ZLmqrWUdRcEEj+XRqNdAXeAZfARiQBvQdHh6G2YQxoDULtq3uJaR5AsPrqEKSUXCj2Xoe1jux8ewYWWVINyoXcKq+9GYBaTbckL5LFD96xaMxJ5kHbZEt/aadPNYRqxypjgOOA0fnQKUiS/9tfgxSlIYfW9KOHteFWGMOtAyAt27daupsBAlxufluuAVxQVm0QyY/G76a/6ZNm3gL3gA8zLSySGAY3xkhgW38DYbj9Nr9LygLByAeQmknzLBqeA0DYDiA5wIAazOKH6XisC4ZSHd2pkHcaK40b3QxYD6bwdNU5CVcSGzbS/zdo+OA48B654DtoGdGNZQcALhpFcV6L9ypRn/LABhGAh5gJ1cckAB84vhuAB6+Gw5g4B4MJgxKrQUglm0EDUKjNJv6SDDTnpMR5QSb27+6rAgJAENwPRTDgPGHqyAuozoR8OoIjvjKuA6b3KgFO+jowCsexeUkJW1wqgEzC6McK84LHWEHwMovd3EcOAU5kAxLIQ+RqPTbHQC3bTW3bAwYjhjqGGtAUDDV7lFwwWBUXpAVhyeABGYYbADMqLkE5oo/Ibma+ssHxyOvbBa0pdbm10PHgGkzxgoKKMRHui0zqmSVErOoZDK0TpbitF+iszopq9VIM0up+us1akEqm0mJr3WBJZ0mZ5xs8nC3jgOOA6cCBxASyFIkIZKzGKjQcCboNq7YVmrAsAV4MAQyFqEK40MPTpFXtsriYyIAmISdlq+KVwlUg7uoj6C1KZHW77MJ0ubTxmxfII3y8oA1npbDDwc6mr2dkkbh7EX0oJAMEqtlqaYnfy7pRhm+Ys1mSTHLkpJ4JLPE4J+8cjeOA44DpwYHTFoiQxCYVqIl8uPUKOapUYoWAzDAYzquQQ6KLD7YTpNHbgiQfEnJK+M+GMwNGMw1UaC5N38L0+ZXVi1TOlPZWa7MkE0mLTuHQDYAHIGn2p9TQYrxHJb/itMmNTraoOxWfPwklGAsG3twDqg3ODBQrVXktAagWpRnGWAO/IAjouxEYU3IXRwHHAdOHQ4gBEyNYYQO4YkbGUE2njoFPMVK0mIAPsW4eQLFEROxHtxkcdFQsTHTbOjGyqvFKSoQqxdwyqFjGjeblQlcsYtiSCp+SGKkw6uFiHE499dxwHHglOSANXm69TgkACfRnJLFPDUK5QC4xfWI5RwARrM3M5EAp+ezRRitCA04ajv4Go7GxIbsS2lrkBrRlGneRCgrEMxRjGyDxSVcsGNHajPBFL3jpNxfxwHHgVOJA4gUoFe1XzkOzsbmTqUCnkplcQDc4tpkphVYa/PRAFkDYFk/pCZoPepJoTUexmHVLxBtfVpsTUQx8zUVKUeeiBME5jElCBydyO0gVznjLo4Dpz4HECngLn16HDfNk2xO/cKvtxI6AG5xjRkAM+gL9NqkKyZOcZgvHVhM0KypF4cqi0E53ohDsVgqbm6+VAsbALDZnRc021AmSaeDFBao5v5vDOKaprs4DjgOnIocQKTQ8zYA5obHU7GUp0iZHAC3uCJpHpigOzrASlnuiwNoUVjRifGPAFj04gUl1uCWkEz2RntOAFjmWUl8mYfFXwaAAG4xcau3veHqTNAxP9xfx4FTkAOyNNFpwOukYh0At7iiTEPVjbBsarPshAVNNhEa9GwoKitsCsAmaEqgRHvGM/GPUtGlTbRDW+bU4kK67B0HHAfWigOIFBq+GwNeK36fVD4OgE+KfYdGXoSFyWvzXe5drYFtmRkTErSu0AviYkAupNldg9NM6hViyf4a/OcYwkTLxder1tn0yi+kfEzWkUP3FZiWs5E4vZAfCA5U45dGs5ZIHicNh3JCksC8HEnGrw4R8sh7DSJvjtctjSjPpCk/S5Ys+S0NdrzZtDR8UpC4FFEB45ImxKl/yDJubpqKbPHjUPYUJ4WvpRa/dn8dB46fA1VfNshDnOTDRlrHrthFoU77F7EgDmMaB44jHuzRXVvLAQfAK8l/hKkJUZGqJlm5yg2HArJzc0MwkH8grR5SMsvojF+Ymat0FaSB+CmJng3yNI5OP+hKpUcnJxtZrxx4ZTajpEWFFaCVOktnBGIPjM4WOnq7/EqXzrrSg8dA71TNT881vC0DfZXZmamSHCqc4n/dY24XYMieJgDhfG2eWyk8w8y1slcvQp6SKH7H6yCGtKKy2h8QXX6C9Oj0pGw/zfJ4k2+X8BBPKZSXVr14WL9CihmGmBuUhXR6ynNhccYLeVQuR6whPDyR4nCxyFzFQwIoxzS4hHDOceA4OcBXiEQp1r1wfnZTId+byVTmi410lq9QDv9lhwAVHhzUgghJIPk4M3HBV5IDDoBXkpvNaUUn7yZeKnYXnsBb9FCaiqz6zVo1yEQrdTzm/JTfCGssRlKJX5UNOGxTV5PYXFkHjNRHAw5sJyyJLCkI6NHe8AzQgBUiJFWJIcPL/AURs+mMJETQah1btieWblFVT8ARS9PWqyUhVOhPkhMv/ksGJ5B6m0WRwsBm+Qn/YtT0Kjr1lG1kGBeYn59jJ0A/n6W/ZeHjQhDFfhY5ukZvpXO2OHgczf11HDgWDvBtIS5EAw7DfBDmvCDl+dWGdLh5heO7RY5wL98Z/32CO9dKDpjkbyUF95e8+fZxsYS1mVCsMkJE2x5e0jaYNCWoKY3ENghrXsWr8Rdd4glcdhSDztOK52pZauSykIISIPZubYRcQHcxRKFvZ3O2w5ao58fpIjVaYwn1VkyudoNPKH0HnshqvZu9KAjdGn5qqqD3FKEppczqiZkMGWRzhY7ObrFmBFmmqfNK2CJOdV8YrL/Y017ZtZlrzf7u3nHgODhgY8D0BRkGRgLYLJPjiO+CriEHlpMDa5j9qZfVcgy13qeU1fBNlGM9VmG+VEHt7MznLFYqhk9C2srgZA2fvpFNOlSe8ycgUWZBY/Ts6O6yZOUlSrRiPAnSAmUdcFX1Ws1AJL+guwQnehm4UEpkqEi2/JD+Mq9OxsU6vKahBHHHX6BXSNLryaTf2rhaEEbi1Qgt6ItjhRglg+0+wwRBOiM2BS+YnKtVGul8V58F0pAAsOm44gejSE2rRV+6i+PACnEAoZEsQwKA6aavUMIumZXngBkvVz5dl+IROCBoCgCH7NpaQRInO1cLAAKOvI0PgKItIa0luHpamoCoPc3Py0YcHA8VR5Jwvk65EEUt5bMVB/1fwVuJAGIkqQAaQTafk8EhRoSEjJqXj3fysGyO+YpBW8eWNHVykPzIsQlcwiClar1468t1ewFE4VmCqjH6emm6Mtl8indzZenP5DrzoHS54RUIIqXVKLo8TGGXqoj4s8CQhbt1yx5HeBtwAMRFDGBCYykwSJx04tuANEfCUg44AF7KkVV8Ngmr0AtIoXNGNuRCHmHMD5Rid+cUf2INGPhkP2j1ULoY2uVlhHDefEnWAXd1dYpmZagsKpa8JhQaMPpwrRJvKM2gpYYhI53tI7kAEqC+h8mUBcMMN0ejyZrXMVzIKcYi2aErdhG0LFLwUPgW4CcOuO7+RvOk0HJhJdNYmAqXhgM2xsaVXyYnPEHpmCl5HVlvYRt8rZe4F8RHwDrthHvrjhGO4PblAFJFNWDPdsJyGnD7VtUiEdnOZK5r2oAmBT8KAUzqPGTBUICxXK2gsBayC8uIdIGQlJYOLFAKAMsqIhJowjd5Vif6cYMDkUSZTt4TCwDkMeXbVuwCwOSL0dogQEI2woomPFvz9ozZsDArhsPqSe3bDqH6i5ZCaabkFVGmoN80aGpFWIdXqw9B37qXZgo5WMtvZE6nk3veVN37yS0j7/roFz/39WuoCbo4UYcIPkRWa1KgwjSdpPzCpaTjkvi6G8eB4+YA0z7QgDlajTFgkNiNAR83B9cwgtOAV5LZImPj9CLciR/1L+fzRrCE9AUYbZcMbMUEth/QyPQd7rNpEcfgq6FmlIwGkix0uhQtDbjNZOKxRd4aBGpoPwhBYBIgL36o0TFtrPr1sU8xQejWeyb37L43e8kZOzb3h34jx6hxlNOx/yGG5RAnr1Exycqz6twy9wosgpCokMeeeBuGxAhBQVjYJehr+IriW+iU+x9et+s7V/1gz8GRfUNjO8869+JLLn3U2XnesiJTuBRVkHFMuOKc48CKc8CkCi0Z9CVxZMiKZ+ESXCkOOABeKU4upMNXL5LWXKzoBClRc2kRtAaQqVL10Hunp6dBUOZbEYUfGm2W7TfUMTU6m0qNjIzk0x5rhjukKcl/gV5UVt8bnRBUY8otMYjDu3ojTIUN5gFJ+jVv2+aNzNKanZlH0oMBLEmCLAaE0z7WT1kYDH586dvfufvOO/pzT9i2+REC+cRcIF3pOKZLYkylcEIk2fGbr3isxJFnoKdWF1t3teLl8ieUxTHRseqBZOA2KM0XM52dcE+WTmttzoXeNT+597qfXX/zL2+dL1c2nnb64PbOocn5a2++/bKzH8hWKvSwMEhInyvp4bCFimwVKimog0cYCVa9BC6DU54D8/PzYDBHijO5BPRFCJzyRV6/BXQAvJp1F6MveQC0NgqKjBWprQoiA7zYpDNpmf3ETzBSggq+4kcU68w225/NiE0AzirkHitTUgDsTgsSHNWTeU+kBxIqSBhUyCyvWMqDwfM1r+SlpstyDITRkKR2/DfkYGgim4SQYCor21YAu0Bviv/1GqNSx59sm8Xw6fR0FUOPrU4o29C8d/3P7736Bz/cc989ELpp06aHPuKyRzz2waWa97/e+dHvX/XD33zGAzdlPKbS5RnzTWOs0C1ZZGuzQ1xcL4e8cB6OA8fBAVN5MUE7Dfg4uNaioAviu0UEnMLZGhotX0B0H+StDc/kY1jSBmOGSjZyYLs4UBYgW96VKrK6AEV5kdyOsRqDMz8SZGSXAPxkzw9xqmYB3bqYZrYaznup0dk59DmmCy0HCxrp6BeiKtRHeQkA81wOvYJkJTq67PUhPQKe1rGrVWupTIEClBgBTnnfvuqX37vq6tHhoS0bBx77yEc89jGXbRqQslM3hWw2qNV/eftQ/8WbZcV1wAaAMrpQrzVYIHaIGYB6twpax8xxpLcDB+i103enZ84krKQT3w6EORoO5YAD4EN5siI+S9EXyEO+8otgSm3RMgnLaxQ6cmr3bYgiTAh9aF4HHOFiMhqscTE00bo6Ojo0uNAsyCYAzKIgsXSmmCeky/BJEmp4UffqEhW9nBHalGQ1W66WPX94egZVFcCP4fuEOEByQgAZBJQRHfEH197lhanHXnYmH5liMCEs0Aml3zaRWFnN6HpG9furfvzjg8OjL3ze857xpEv6sxGqwm0g+lEPveTq73/vxz+57rKLn8G4vhoDsAPIGIGF043NpG6FZ845DqwQB3RqCLM4+TkAXiGerloykWxftfTvhwkjTBN5avfJ4wI3jO+2QqAjz2JRsEmHCnXGBDDF+A34yqgwYAZ2JsDFMKIh7iyLgD2/o6s7SVThEwVXwpJgBgz2A1uGhBfinp90A3jQRUOQNVcN60FmdHou6hYkaR3PjU4tQ6MnT/tJDvtHy5/+3H998RvfZqoX6rUsZwayWOZk1B9P+m0VNp1J16tl9tdWlnqzs7N9PV1PfoKgL+b1gJVcda9XAfjZT3hIbW6CUeED09KzMbUYtVkM0cIFqfMmJ7XS9OU0vXG3jgPHzAE+I5ubmcsJBiND3CzoY2ZeCwIukQItoOD+kSVKE01DHFKWOxO3BsCFgi4Wld2YQUddroMEz6MnNeq6DtgiSgr8j/d+tLkVySYeFkausr+zSHcbBKI1JnJ9YfhXiQGQi5VqNUhNMBdM4wOlJ2gHtfhaMCva1Gzpvt377927H80PH5mBtc6hVzlEYRqFXJaZcXD44DhWhUZfV6GTtb812cukN+31prw8BoDp8hn93oMvPGdmbvamW/eAvkm9Mw3OxgUMgxdaoPEwysb9cRw4EQ7wERkAqwYsAkMeTyQlF2ctOLDQ/Ncit/tBHqCMAk2CeoctM61CO6dhno37CWUIbVdZBxw1nmQRAQuU2D4pfi+beBAJS/VSXNMQeGKFxgRN8zMKIEjObhCMl7yAe9plqcpxiME0R6acsPK10LgXCMFv/4Hhvo0bsh2dU/NidedXLlfXPwY3GqV5SkOzQY295649YbXU29XBOHdXWn6p6lyqNpsNa73pBlboRz7sgewRffVPrmGatPGJWeiw2md4YKkGrLW0wEyrNHd1HDg+DvAFRWPAvkx5BIBtIufxpeJCrxUHHACvMKeZwsxPZx0tWGVpFQ2dES1DoYyRap4AI7ZZBmUzLAwSiAJfU3VZBSxjwRyQlAprRXYY1sFjfGSkUE4kDEmEuGAmOJDOiOGZBFXDRLYj2YFzPTmYiKw78kQDxrEsEJ1aZgHJAmFOYpBjAquiH6dnKmkMxTwKHQswqtGOeiEzJU5yECu0JEAy199+b9CztZHr2TdcJ3E8aww382J9uyDIdyDh4DL9nJG5ctjZPzDYB9aW5oopmXqFIiwl7CoUCPPohz2okM3fePOt+yaiNUvlkoA3nDKeUWs4alNqX7zNQ33b5gJ5c/rjc40qcKEeeSmmG374LXi3DfH3K0LgPxVBk2bJAWYY2Uqgwd46cuaRVo1JgpgltkajaaVG/ML9XTsOiMR0bqU4wFeOxZU2IN87kpZfQ/AYHxWuNb9S5RTAdE2EFp7MfmKUNiVoCGZmi7UgTGfLdQHgwU5sybNjlUaFmCRYr8nWhZVS4CHoG2hhY9WQkxzynbJw2K8VU40SEFevZ7xUnhv0s55uThGuAcA0P+R6o1rr8HLMzJDts3IpP+ONTwLoDTaOLXp9oxVgRdmgLfW4GFKqVmuQxsQuOgOVGmuRiX7fRG2msPlg2Z+u1MEn6OmUFVM6B/u4Um+vwEHYyDCSna7Po/X+bNfkaGrD+Rc9gL7LYCf1VNap55mKn+ZcdCpxQ5f3sIsfPDCw8b+/exW9EGFtpUwvpzw3K/0d+Unx5llbTM8pzHggu3Zi2qfQ0HzfaO133vaZ//2pa1lPyjdSKdHPwJSCg3r53vnA8OKO5+P/fNqnrOueEpg/y9cVprpStV7P6w6YCBKOzxYjoSTlo66kErWtU13W5td9wddvARwAr2TdmQDiKnIo/slj5DBECgKByTj8K2xT5TewR+IBfjF9mQbBPbXCEHDgV4thaOqjLhrSFDkMQOzH3rzsJRnm2G5d1F4gXQSgpUMSgsokFfikbwSkw4wouPx8ObEHiVmpVbOpdCaTr4b5CUUEVeCa6I3pPvxfSGbfO8FuncMdBYSUkenKMIuc0h1jkzOkCD1qETt8SuvljSwgo19Fv8gbmanON3J9gwOykYoAkKq/forywhAc3o+67PL5udINt9wiks/zens6uOoBlNoXUWYTWN5ybxUkAdvCQU6pVD84OnvfZO22g3MAsNILbfFf6WzJV9dmhLcF99aeCCqjiCQIsZ+F9HoxwjD5r67nAVNB6jC9SH1J/Yk4iOsxeuv+rDUHRHI4t9YckBW6kidTrLgijgFLTEHmaVXCJEZGcG0WtDQeneKsV8KKvJuZn2Orjq5ONDGMykTC4szWk7yUW9LFKyOnEKkKHIXRlxqoVvdYyGSHhjIYPD6uEU/oYuv946hi4p6e80rzs8WZaRZCsJmXDXxKj0B6DuvbCfNAnSA9V/XGRkfCRnXHtj7lOhdYbrW3UMZLLsr3dHWMDh28Zc+kCDxdByyjAabpakytvIUo7XWXTk3MTs/Ozx8cHppXjZ3VpTGFUXkphP1if/e3ZRywswflJGCZICJkMAbsYLZl9XG0jJfKi6OFd+9PkAPxLByNrqOztIp4FjS9VRlTtMpAxNN4wORUkAGAZQEPAg5fCYH6xUxlUaGnJmdSfjjY2xXBs0QjnjpdqkRqGbaKCAMOGySMwKQE1aR0jvTc3ByNk5TZOmdkdJLx4RNwlEt2uZJkNbYcUeAdHJ1nqwn0+VwmMzE9JcLAXEJh7LEe/+psuNzkdGVuarwr62/sOlIhYM1DH3hJb0/PlVdeCWfYISslQ3PSTRG0Vs5wkb/RnyOltsbv+PRYOFasVFKZ9HyxODWrmr2MkBjh/BV7AGXkp+VJ/NeYUpddxAGbcwkA09aZyEkDxznutC0HaDXOrRoHDi9SeSMAzNHZodeZz0k1cKS7EmIghQ/nJQCN1qXldBOBTwFJQrHclBHcqVTQ2DTQIwAtcZDmogELOHOugk3HSgcYOtkXGj9Trwmr0CjbbszNzjINWxY2kdr4uG5qqfZVJeMYL0Q35BeDFjToLtN79g9hc+7tzFer5ampKcqiqj50iil63TsQJ8jsHxrx6uWtA72UTqpgMQDhaT8q5lcec3lXLn/rL345URQAk2MqrKp5h0HQarRdmYJhfWxqOkjTr/J375f14myBurBYTfV4A2D7NNu1HPcXugBgutRYKagpTBWgLzKET9G59uSAq5oVrhdFwqVpqoCOPVXsgla0kHKZiaUNWcUi+CsIxoBtoiXKEiPfLzEILDJawohUVxWTPThGxyeyQbB5UAS9OE1WlRNkuqAh/hnAVmBehhihQXNQ1UtjoAGTaTrFrOnU+PikZaBvjvWyTBRmlHnern0HG5XimadtKZfmx8YnIUYPP1SFb5k4x5pdm4QDdKi2O+7blw/8c3ZsBniMsUKe6rVaFRGxvD1/p9edzzDf7eZb7qQHgklDQsXOaopgbeigjR03RyZnQmR6NnvP3v1Urq/ndEXUysfKqdMeCtf6r9g2rIHjJqnKWj/fTzRg0NdpwMfNxDWM0CQJ1jDX+29WiF6kmjqBpSqzZWW+lYixhi5QiQ3RvGVsmIaEBhwpWLqGCM0W5MaSOTU9y1SLDQtnCy0or3UfxVfSVPxlDDgCCMF4vTUS2MqDnjJbeTASPDk9Jbk0I0NE5tH/CPESNwpJh2HPwdGwVmIbCoag0YDRosTuakr80dNr6xCwTnpOnnf7fXu6cqlzT9sMuZmo7FIFBIg5ITc0MLT+xzyCw6Yy3/nhD2XuW7wrt5RTguqaMXloimmPrb7KJ5Hypovz9bCRLeR37R2i7LHTr1I7HJEPJbcPKw7h/q49Bxiykl41JwEjQLRb72zQa18Lx56jA+Bj59VJhWwWTdwjvZBl1jnF9ks1mAZsUGkSXFqRnKYgIcWptgGoEr3cYHpqhfXDtDGdQ81r1S81m1CG5FQDVrtwLZR5j5GLsFjOJWSPDo58yBY62BeCjS2jAAl6xDGO4S/9bJlEhsO+CsaMzxSzqfDCc3cUcplSiX0b5XwnXI29OGzykTytV0dBKeO+4fFCqrF9oJM9sMRRLjHGU5PSphIu1ipSGU+/4oFsYHnDzb8YnWPptvaoopqSqARe0ICTmPKmxc4+m7lSjakIFG7/6DhlpfgN9KzIaXmpfn4LH1n80v1dcw5ITbFVAE4/Kpvh0dRtWnOCXIZH5IAIixVx2DMtHQMVphdNTk4uSRmJj3+MNNJe+Vy44p9E55H9dZOIpGavLBYTd+0V/jaDyaKjZnFjPpZmksKa3ygQHZIrslkKjIBmDYuey8tTtV5jZ4ycasAFzs5F/dVwACZyuLOzk1VKM9NzzDrV+dIm4mWW0/U3HhzYsPH0rRtoZrLNsEhAvKU2+cvRwxDBMbR9PR3ouOMTUwTDR3ioQ8GQwiSvmalpOHbxxReXKhVGlKWVnoD012KRsA4hixAenfeGxmcq89MXnOF15POpTGZ4VFarcFxx2uZlktG6ddgQ+GTprUzOV+cmRy88o6NT+0/CfEFf6YcoKFFCGXsrZFmVTU/Lu+JXHt/IZD77xa/D53kW9KgjMDXHwu4IgE+A/1FKq/UHCvfsPbhhw6ZyqbJn/wjEy3cmE6F5k2Cuea8WDS7dY+cAZi2GrpKZ6gAx90nv+tjTcSHXhgMislfEgRakUyqVqHLgEPNpX18fUAocApYGn6h0suTG95H7XAmPAoYZFn+LTkQg1vY3NkwlNV5xcL2F146dByRzQ1Ig98GDB4ne29tLavaWxxUp0QkkEnMTfTCOLTfxwrvYD5TiJ0oEW1IJIqsDHPFtclYc0S4loKYok7BkW4+5cpVhuZ6uAoJQZ2fpuGKcqYlDuGDTj435CwmTi4aksuBVV1cXTZQ6qpLPYgIWohz+zganZXEhcdn3jjk7rEFqhBv7ujmnqbOQY8LR7DxnI5GpKYiHT6vt3yjnfFZtHSjKgsv+rnxPRpc4R5RTk1FlxlUhgFxIyQbRF55zXrbQedfuPRNzXq4QRbCNvaUXdfycXwNuQRQfRbkeVssVvkZGr6NeNi+UYKpb55MRSuwANrdsDQhzWRyOAyYukpWB1BrNX0XI4WI4/1ZyIIaMk6ZhZmYGKKXzRResu7ubK58Cwh2I5SMAPg2JLR8ewU4DBkS/SH81aoGjnK9nwENcAk9MTHDt6emxwIAujyA04dGwwY8tW7bgwz2AzWdHpodq3gRoByewbDiq1LA6CFUY/oCUJq9BKMNgOcoX+OREex0nRrxZ/4Nw6FKIuskZNqn0Nw0g2GMA0A4N4Ylo4pGsmIQF32ps96GeUSIxzM7OzsDA/v5MNp+v1OrssyA7ch2niwCYiTiaKxJ53yjDnMH2zRs7PK+vt5v0JienjzPVdg7eAIDv3RcCwNs39XenxHqsYATN1J40KGqTP1zpl7DGi2Op6CdddOHOjVu37R4e/dlNe+LwapYQ2MJ+q6xPcLttGMDHVq5ibSp2dnTUwmB4Ugsbr2yhupVupV9ndCdFa5sS3L8IQa4iAxMNmBuRANI7cq4dOSDyYkUcoAsogrKgI1XONemFWfp8CnwZhOGtoE46TWAe8QRxeYuai2ZsRmb8DYb7+/uJjifggcZGAMKTMuHJAh8cAdC2AW/8cQMDAytSohNKBHEEqsbyVJNYJJJEC5QQCF2zC8GKpA5EZMcimL+8QjGiyCQj2ElCRNPdssYmpwHLTRv6FbxBdvwBAoEC7KASUAEgm5E1vgCwEpIkEikq2CdgMrYDOj2029nZ44dfTVcs4+KESnLaNzzdCHLbN28AdQb7+8AkJkILXyIi7M86vsK+e/aPVP3Ujs2b6CJRbq3BqObggD5aiRuYYwhDNfVlvJ1nnc1Wo3fdu0t2RtA6iXjCvT4aaLcPayCP1sWyI8Y4BgcH62Fq30F2HcUJ4UY1GCxmaV92s7ZCiIdzLeKACVhTVCABGYK8XRiybxFVLtvDcSAR/ocLcKz+SHDkOPU9NjaGWEcNRQkmMh8EN+i4SGhQE4DEB38wla8EH2JZHkAyPvaIOkuCFhIIQbHmSgoEIAWzTnNPRrwCg8FyS4Qo5GVZm0/rrpF0TQiQvTiAqNgb9iDKGfoVC6QEkhcmxe1KARHmcogQf2Tx6IKwH5mY5hyjTYMD1F8o4k823JA01JleQrK5dEoAmCEA9Y/QnePkNSX4RuNk6AADA9wen5iwWdNRKsf2RwpFavQrIBoLhADwhJfKbtmwgVw2DPRTa6PjYwQS+k8JR13sZpp3kNu5fWOGKl0ol01Ek0LSy1Jv2FMnQKUsNbvltNMyhY6RiUmpUp4jlRGeGRfbkTtFJl2EKaZw79x+mh9kGA+WjzNeVM6XJsWUci8dZ2nHwtwPaEIA0uRVdEhpueERv/tB0ddlERek9kmSb2BJIqjCwCEqqRmKTVXlI7D0gUyAlnv8uSL3QWKugIFBLyFBhQ0bNhDMwohWG4BDZT4mApM4yi7DwMAwAYgI6uNIjYiEJAuytuxadF0Qyc26gklZ9oDkNb851d0pFBLN7Lf4CozFsa1QlDf2UKmts2DGJqbQazdvGOQpAoAoYjT8TBSqlq08yA38VRkfRYdLxhbYHtZpqx42A/TU0dFxUc2Pk2UUip8IZZyW8MDIRN1Pb90iE8TEGhH442OTJBvr4RZ0vV6Nk/uGxpnNvGPrVuwR0SxoLZAUc6FkwsuwTDe0gZWD6hjcsKlUC8enZzL0rJoZLfdRxS3Ebo875lZyLldnoev0HdvZFmvfvoNCrH6mlBQAVg1Y6dcyNRW/PQpwP6MCOUwDN9FB0WMAlk68c23IgRUDYDAP1ZMNlah7tNLf+73f++53vwtAYlgGU7nhyzA9FYxMGIF6BO6CtW9/+9sf+9jH8gpL12/8xm9cddVVFgagfe5zn0swbNGMCjMh64/+6I9ADm6A4V27dhF406ZNoP6f/umfEgz0hYYk/ba6MZFrXREQq6KqLZwRdiyWW4Zn1lORLq0Ww3CTRJgLMzUzg9+GwQwgp3qUhLD0+UN47kmEwXeYL05T0EC8kSf+2xu6LoMDG0+Yb6i+1rGwxBkfPTg6Vm2EW7ZuoofV29uNOBifmuStCG0rmARdr66GQYHTjodGmfe9edMAXMQCLzUoF/mrzmqAW+vSNGRLbuprc3exUmZae9RhEcZJPURLuDRmu11mZxlfSHUVOrZupQZTB4ZGmncW5mPSr07Km5S53Ypwv6JHetVhiFSxUnPDI573Kyaso8ImIuNkaaaOEeJoPCip3/jGN/bs2fOMZzzjAQ94wGc+85k777yT1O1TsGwQ/faJEIWIIPctt9zym7/5m7/4xS++//3vg9m///u/zwgl/gDt8PDwy172sqGhoZ/+9Kf79u37yEc+Aioblj/lKU8h8Pe+971//dd//dznPgcGkzs0oBafbHlONL4yFFRC5dGFuQo6PLDblM6sYqIOS48EiapybgkjwLxSSCSK4ZNesRLkAsyXAXAbizkZZmNnIiLWSvNZr9EpKWpiutJXxLr+bNCREoRBvs7GHaFMnhazg/hwOCgHKjFRmdyDVKNSCFitlGYEeXKOhI/PKZjL6F8K3V0S9GWi7MxYozbX358nrZ4sZy/6c/NlQAssMml9fHm0S2hBSkrQqGUw6E/OjNf9YlcvoyeAa1WNEALE8AFW46nISvCU7IjPqrMapxh62zupjPpkqTrL2m5eSqXwleigPQ+YH9RTXrSHo37nOLyS2eyZOltec/LV1Mw0K8ilqyH9Kf7IF+cFOX4sr+Mbk+JLAHH85WdaMld7tFf6oJ2P6Ln9/hi5XNUlT5GH/Gk7+iGqHrIVHWJVunzIAZY2IkNqDTmhS6uLLzTau1u/NpMKWkJ3aQUHVgyAId76WdT9ox/96K997Wuopy9/+cvf9773XXLJJc9+9rP/4z/+A3sIwWy6MjcGk9iWifKFL3wBpfmiiy669NJLP/ShDxH3Jz/5Cf4AKhiMQRsdFzjfuHEjEfGhW/elL33pvvvu+/SnP81KVrTkP/7jPyYLYJtXqOAEQyHmimvWuc1n1a6yTTBylZkqkUakEpmjBXlOI2/L85lcPaiUEcdTc8BStq8jL7bZlFfxvSLrPbBp6vhhUA93bOqphqkDk9gwWfRSDdKiA4Old94z1hlUz9jcR/GkXaGSMA7Myds8k3mjkgsbIAMiz+8aBACYSd7J/bwIPXbeKmdSdE8OzjJXOdvrFzehmfWyFUdj3+gcA+nEOnZH4JKYoNMyV6fGdFnvnr2zQX2CsVEWN0PbeRuYA10cn5wnZR1gBuJVFBx7HqscEp4s+TVlCKmLf6GXT/k3XDdSyFdOO7NXzjTyGfJgOjvj+ZIQvRtEHX2kqkRkf8Z0qcHroCMv0nAHn25xfqqaHmZunQI17SErdUbFyf7QxCGZ9nHULydSl8L6tt7aeT0crlWdnJ05yGYi0uMS2c1ID+WqeYW638XshjRdOyuDWkW4patHlfOjwzddatB3kQLyn+Xq2GXENMOvjVxEXlQNSp7ecwc3+Am59hbiRe0XjzZxkDJdrPrpHGZIJAxCEEHhpXPTVVm7KB+ZfmzST6II0uPnK1xJCGgTPqwjMlaM+9hLUVhtpBarMsCJifj1r389qu273/3uL3/5y2ix+Lz2ta/lavorAMlcLdAUfrGKKeEaU6hwO3fuBEpJCnX5X/7lX8DgBz3oQX/2Z39GXJAeLEchBrBllFGjP+1pT2NNMMoxcbniaQDcbIFJBqqTvFb2RgFfWMrnjugRJ4qhWGnjkVo+/CpaKIE4wb7BbKVADiiwhs1elDQhaRp+PetVOcuEBlNrSFQtC81dTv2dLRXTXqOvUw8xtFziwkpzIumwjOyXpEwDroc8iqKt0gJyEIhzon+nC36j2/P6O1ksWJua5dSG4xYnRJFWTWakm/bGp6cyYXnLBlIVQtikoi/fwZGks2y8hVfEBaFkXTmKh6Me5DIxPpMO6hs3duNbAUhCbApUoL4niISKx7vpGAVpDi+k9GzIQa9oIJ9jRe2onsogyqNUkkyM5ye3kkobOeiZmCuCM715H2ne39PBQVyTsxzJYAIdEJYtRKhZqVzIbxrrsKrmG9DCAd4cOiKnY/IRy7IY6RwHHpukqf2+jcrcTEpSH8nNorf0JpZ90Rxore+xT9R1HgycFwxmYaafKofSW4JWvjF+9onGKsJaU+jya+YA1bQyDlwEgMFFdFBSBDiBw7/+678+66yz3vjGN77iFa/44he/+NnPfvY73/nOE57wBDpohCcwsAqagpEouDZSyQgu4VFqzzvvPFOpn/rUpxIXOzPgTfRnPetZpuCSPgoxiXBj+jHhb7jhBrLGvk1XwAAY0EXnpkOAwm3AvzIFPs5UopZqSrlSxvRm/kKqQSPp0SNN6sNe4WmdBizY3Aseo5RwcEIQDg70mXSTeM2CQAR6AALwVneE9dlvS1pdnLTKe4852EBHJpOiaW7olxRObP00clQcf7BEZ7z9B4eAkjNO22655dIMA/fUGvXRyUU0WqR2uxpUNFEVsyzykrJin9h3YH/QqFNGnvUTjfkv7yOX3JIE96b48Y6pDNQsi9vj7yGO0K5/+Sponr3dXRRk6+ZBNm4bHhltWi8uLKKAWl4uizimanKDsRI6H35lPs1OpPNskTbjg8OifgVeDlhfD05Hj6yYUQnpR2iZ2416PshkgA/aEC88miBtN1IdPXBAekYr4sBUHEkxVerzn//8P/7jP15//fXbt29H/X3oQx/KDZ8CjRkr8cMe9jBAhcCoqkzdQnXmBmhEMyb6u971rmuuuebmm2/m3qKg9VrIhzzkIcy3euYzn8lKJxAXNRdZxtX24ti/fz9RwGOupGlmZ+D87//+79/73vfayqUWzo4GeUHBlIGtLtcxCzwE06RNHBPGGrX89X1YhBpBT4IS6Ugxf8UNj47hv3njoD2qMiErcZseZf8NksjZaUg2touaqrOsVUdmLy3mSwm7APaBLuA9lOG9mJIoqWP6o0UilYro8bv37mOW0k6ZHizCGJr6+7r2z8vhiX4/9RsTeUwpt10gOGYAzH4op2/dJhUnBeJv7HiMfCJPKzBjvTKAr1vK+BOl6ek4fNv/xTTFp8VCNajfvnnTXbfduu/gCFqrfMwx8byK7pMvOHqFggvPxFSbwQjjN7L0yDDcY6evh8VStbMz24ZfhJkkpE1Kf1j6rtxTMvoT0auodHFzjR7b4g+ilbZvohiCuOFRO/EinJ1rNw40yY6TIw04McMvHa5XvvKVZ555JrOgf/zjHz/5yU8+44wzQFCSBzUvvPBCBmtBVh75OKxrBl6Cvtw/73nP++hHP0pE5kITAB9UZJIFOPmGwO9f/dVfxf+rX/0qcdGSQWKzYKN2C5IFAXkhMrhB5eXLw0D953/+53v37oUqwlx77bVEX2WnLdbykPWRkYvw0WaAy9ALG3EUadZo89SBNHbEkrRoFek8KgATzNYBa/QouaGRMWyeWwY38JaIcmZ6ANLKrTiRGjIxij9ZmSUdlCs1SZQfJu/or1eUzSyDrk72SfS6MFWlU6VqZU6w/vgcKlEUIS12yP1Dw5Riy0bZIQSC6jWvr6/HDwLW3sinBh1tZnKkKyM/ZcvhSw7tQj7oW6x7Q2OjHH+0rb9XfGUAgWpDn9PYekVfkml3WiExd+QtXwPdU2p2JtkMuvm1JtBuF1ouUzF6uthX1Nu6aQP1t++gnIlkX5td5SMTuiMuxUUAd4EsMJjvAmuLwHDSV2HeQjqThZ9t7ygU9QbxQqnUqdyY+qvlbbPvGTmJrDM9BEK54VEBWOh3rt04oA1nJYgCSMBCFFnADzvwP/zDP1xwwQVYg+k7kzzXkZERWjL3WJjR6vgseAR6wVfD41e/+tXMvWIi9Pnnn08ADMu8JbyprYQhEWZmYXZmNhaxLr/8cpRsgmH6Jvf/+q//Qj8mDDBPLBDalEKu5MUV8kiZV2vrTEbFeYruIKiMb7FcQmblc9IzTYS1daq1ZYsGzBtYSuCknog8PDJOlE0LGnCUOGF07BEwQN2QKPm0qMJsMylDlAg7znaIk5orMeIbwi18MBr0snNZKi2G4iixY/qjSGN9AzGmTJbk8HawfFMfZwxLoajv/v5Och2fmNFiL+D1MWWwBoGE0GPMRrYZGZr2ZsvFnnx+U49wmD3cFH01iYWktCtFqpGOJP0fqZ2619PXS9+KqfvHmGXLg9GRpR67Ojr5MHZu2cDWamjAIGqzo2hafrUqEy75hvg06KDIj2mIKLt8z1hEw5nZuXSWzzv67JuTaqP7hdo0oih08sNHC5t0vNqGbrCWJt8MwLEG3DYkOkKaOCAIt1JOoEIHIwFCQJG+MxZgGjDqLLjIFbnDDbBq3weqKmBp6Pu6170O3Zf1S6i5gCXBSAQYHh0dZSETVmu25rj99tvf8pa3bN26FVs0ND/xiU/EuM2CpXe+853ouFiqmUfNW14R0bp+9AZI37LAn07AShX2eNNBdiOnFAVV4cJk27QOWEaUFJskTOys/0GLMoWDIEg2rMSjkzOpsLFpQPawjAQfoC4LDSS2YC1XHQNmhhd/K9FelA1GLlFAkYqkM4MFsM7YHtOkZLJGb2fnVHbu4GjlAZuPz1QV0YBoygJOIadEdHf0MgcL6JWjINJef29XNawPT04kYlkJbJvLoWQpT40+5aLdSv+B/3uGpzlxdXtf34DaXcXSryuJRL0ToSxVgvoroUEf4rN9t9Su/KP/iUWHz3tcuLEZL3FcoaEpU/Vtl4stK+jsyPOd7NgiNhXWec9ppw2qtczQLsWVjogwM1IWecTEwgv7IHkzPVvu4QjllFfoEgMYJa6UKjmOi2onZ/Ug5VCnvUvpuYo2jwWKDjQhNJCVK26DcYRW/8VqiBBOJB43PJqhsdWkufyX4YCI7BVxaJlobFbx6KPgH1ZlZjKjrXKD0EHfZRrz2972tuTjMFXVYObv/u7viM7SYbAWEEVb/cQnPkEioDgzsF70ohc98IEPfMc73oFBmxFiQN1oZlYXqvCjHvUo1hCDvm9961vJhQ/O0oQkC5Z8f4i/FSns4RMhR/s1BaHdWrPGjxs1B9PCK8yN4rwEmRkqjpoQ+0DkpF4yMlomZwYn4oAbkA7sxOLc0ynhtf58NruSeDoXlzA8kAlvMwLZAeuT5HXAdOoq1gDLbp7sxbogY8Bkg4ANshnmMEs6x+MkNQyNnHflexOzRQ6b7MxmuwV5FYNJucApOuHUHAuY17WD0wInByfY5yvo7+zAbEDZ+dgis4LVMl7CEcNkuedJt+6QG1htIybso0JSSbW2M1+YvMgXSNukQjcWxKYyNVtkvZrVZlQEPjw7c5pCykdIL1G+QNae0VBB62nPu+qWA3//b1/4wreuYZSDT5Ad7fibY/ilvbkQUwf6YjCi0mg0OJkLaT+82soh9PgkUTyMKm7kC40lYVuR6oiBAzSrlXGovzKcqRUPdqLRvuY1rwFNUXPvuOOOf/qnf0J5Rbv9y7/8S7RbhoHNHE3eFoWvZFk6zjnnnKuvvnrZV3ieffbZ3/rWt5a8NbDHkxt7lXyOazAWEmUZ0bSoebKjVVCvgnO1eq2eSjM0i2hjWizjYQKDCCSkWL2WTmfxR3cdHOyl5bCuKk5Tmv09I14638FGnxFuRyXkSFlwAfueSDemVzH4C0e7O5mGlZ4tYutGIjLsRnXLMnx4PY4VNJ1CswF9eX36tm0/u2XXdHFegCGuC2MgNOCsT2O5JVdaObSh5Pl5f7bi3bF7TzrfuWPbZtIkF37sGrVl06CfSU/OleZKsi+H5B2XJ0mnDW+oAvtEWWBN7yIts8UFde7cvQe0ueisswBgDAoZ2fGA8gjbVCaL8V+6PebQDWuNQiYb1op+OsUHwITBEpth6aK7cq2aZbIgIWE4tdNSMKJp0DOmq2oVnRSfxQMQuHlDH7VJtQ70dM72bbjlzonND+i3Iorxpc5iUywwaf0eal5xzuscAIRBXyJzveuA965//s/ufObAxG1XPOkRPdItY4uIsnBQ5gkKD9rGWZtVG4bSRMGthsPSvJ/vYGv7kh9mCtmpqteRkY+8rRyfFmN29unyXXLDow55bG8rOh0xxoEVA2DQF8GN7ZemCwCjmz7mMY/B07JhL46/+Zu/YQ4UOvEf/MEfAM+ncAUwzCtmyMih/sp9JJNF/UXQiohGBcVCifwxCcQLfBPRTRRkM4YDwM+0DQQ9byemUGjTmI7hLFE0ZWx9MtqGEG9g9wMnNHuTmGimbI4jKcjqX+hgWFjMaNVQLIQGACTVnc9hIp0plRMAbu6+JD0YSWGxg3ghi8xT3tgserW3bdNGcJbcrUSdHWnomZ6fj5DfiF6cSMuehN2RwF2gQYtDkTE4Clephvj0PYIemJwoe+HWwUEAmNeEVTMzsUEcmA8eSUHlhZS0IYMAqE+SkaQEyMFYhv/xWMTVGIg1lgRurbPal0WllN1vMFGBikZYbBrou2ff2OScfCc4rjLQoOXmA+AzE4HCMWXFcqOjMEFXJeXdMe79nw9/dqKcnqpU+/oHiUKw+fk5+YLF4SENpD0cdW72KiEpqkMhD5Jph3VpU4WO0E9zcti9B+fP2yGGkLZyiAvoSdQbu0m61G1FqiMGDqzYp08ni0YL4uKYKrV582ZputLS5lF8MUTfdNNNjCeByoIosUQ7FetAZW3E2ejeiqktQ8eQdOUOG4rAMYx7pkNREzwqPBEQJA3QV2k/sItllySEL9f9Y+MAJ6cMIekQB5oBw7rERnBIQ8NHpIW+5aAlABicl2Ayh5q/sq8Hf0rsjsg6zJycxYQQ6e/uBk6YQsUrISM2Hhy1ggAo9hIlP0Tt/tFxDtY454zToQ2rN8s2ALHe3jw68sjUzMp9a0cl6ngDqNgVnsFDruISDoDBdINgCz/MprsOHgxT/tnbt+Uansw0Q+2V2rF2JOtjCSYpyR+5S2n3yx6F1ewQlgpkYJUsdDWahDpmbkvg1XFW3oVSK0kM9dBURYtC+VUNeOeO7exptXt4jEeqHeONfst8cdKk+Yn5xU9nOgrsl4UZ5u5x710f+C8Gznecc1GxFrCxJWHoCUaQYB0TSbt9HDUjlcd/q0Z5UMeUUe1UBjMV7z+/9qOP/OvnvnL1HdHnEodp+V/qC94mw3zc8JiMwbWcPEfAEg6sGACzvsKSxpaFkZmVQgx3YQ9hIjQjr+jELBAiDPOc8Una+RJqTpXHpM1GBUqe2bcQL4pPu50vFrkBgFWEadP2o+W8xh8DYPRkDrOLt1byRsY4bTezYXBQhb8IfhEBMoMLXJVQidQIWHaZQf0Na3UdHBZa0GgEfok7zaCsH3AYIimoaZF9nYLh8XFekbs5mi5O4h3GEVg+IMKk2GrBu+/gUKlWP33HJuywMl6ms6D7utknODMxMyuHGLSbi+FW6LKCLi5uRDKsVMon58PhmclsR2HroJeqibYHB+i4ADrKePhvAePU8E7AVdnOMV3UOBMX2F9KoB27BJBEKq12i9Rx/QagaHa2SP+PLrV8tTp74IzTtrLd9717hyixzOg3smWtEZNvBYCn0Y2DHKXr6PTmQu+v3v7Bgwf3P+2pT3zWMx/X3VWYnZkiBsUtdHSJZahMl6bdnPTGbFoZZaSAWkYqSkWl7DMapLPe3uHJO/ePHpyYi+u7XUqB+KXKdEcG+aL52KQH7w5jaJf6WUqHWIxWxNGAZcWC7zPk8OEPf/iFL3whVzaxogv29a9/nU2bufIp/OAHP2CD6CWtfUUIaJtEaJLaVg9LEEqpSNz5UtlPBUg3QvMD6tSb6GzXIQGoGwbkWEZP8xHDEuO8bIM1MeMH6YHeHkJIoEOcSEbzDBusCCIis50VSIjNO9HXcFOzyI6ASViMUqZJkLVbAQf3jkdxJYiQhKOyqFb1WObCC6tNlrYOTUx56ezGfpmRLQCckiIgvAsdHdPT4+jA2/uWSaHFXlbgCGEXVRxClxktsneKhkEqHxweYpbZjk0bRSOclm6PWCGQ1hRWUoiZS3iJIj5SOrlEDASAqfGZiTmmYW3plexQWeQoDEKD0IdlM+9X11kV25VK54YrHWhubLKkHOER5jZv2sgu0Hv2j1Amsdwob+SCAqwLwbM9XdjoOZ1hquz9yV++pyNT2Lnz9N98rgyZ/yfz8ecmKAZxAW9Sq3q1jAHb6hbu2FOnyvgtOCE1eaJHLGeZqKKf6w46+2opDjtpLwcAU3G6HZFQzjxWHvFsLyodNTEHVgyAkSOotlQ2BufnPOc5TLz6wAc+wEFGoDJTlF/wghewbzOZsjs08tqWCcU0nIJ/FwlyLV/SjGGRmTTZugT7rW5FJTqkyF4xZUYymPFckE0tSPMIN1NEkQ1DY+N0xm0HbMsFTwFsAYEoE/6IXGBDnMiCLWgokoUX+iOP2bki0VjfybTVVJju7ckyS2iWs5kl5IKDWrpNR8ZgsbL63ug4+8BXOdlQrJVxcViHQhm7enuGRmdGR8teb7sNmSlwRmxbKHVyR/EJwYWheni4a/de9g7dvmOnom0USjga1bdYIKwe46ogEj/4E1Ur3REsQ9OTHOg8We/tB3g1C0kKPqdkRlJrXDMAKyXSJzAA7tfF4uyYSl329/jZjo7J2flizetKhAeTsGR7SbEt8+GFOe++Ue9v3vPeoFHb0Jd95588k3lYFCxVHAuqJUYlGPtQ3ArShe7WlPZwuUpdYpqIaoGnRS6NOdfnZK96PjUyNTc+X71nP8dqXLgoTKsfUHb5okwDhhY0YB6dBtzqajls/kkbOmyIY3zBQBFrdlmtCwxzsi8bcbABpAEtGIwn416YoBHlzJemX3aMyZ5KwRQFRc4yXIqDOdlMjlNLkFyKr6YBa4kRh8y14qxBNVmDrQbAiK3h0VFOvduwcVDTkMAsO4hRW4KRi4gRXvjs/yeH7LAOVeWdpmwJyTYgFTTpzq5CoMtoerpE2y5XIYrDImRCrCTQ5DTychcyV0F1YHiIQbKt22WDRpGvhjh67erpzhY6RkbHvbNllXb7OSzmaHMxXYKuAR1KGy/AN+Hq7n17g3Rq6/btMuMKDumcK9BXo8IFIgoGi4v+xPcKwPjBHNoC7GVvVMbHm/claS0AK6HRxQAYwc0iQLwYUeLcrh7m07OMKud19/RNjI0PjZZ7N2TTaSsYM6HlgwGAmUZPPX/iE//GzuvM2HrbW1/JcneOhi5Wva7GTNgoNSphkGOsxaOvxxR8oiWMb6ahlfeCwVH+UqNSmQGtyKOfkesIsim+7kro57r6g0xHcz23kuY4bx0DZq6fPEObmtAcAMfcab+/UV/v5AnDygH6MrhFn4sdM2wxA/dIdCQO6YO+tGfUPjxPaZMIyChSSQ3G3MJhLLLSomWCMsK8IQKeNqzLgFmqq7zHD+OWrtcVfYpDPVETiMIyPjCUaBoM6T4zPRZ41W7TPgR5WQPM8hYJyn9C0fTsR9aNFDsX1f06c2VojRjQVBVTvG5US8xfzWZYOZYlfaa5smKJE+UwVjE9i4yEWMoB0UK35CBe9pNHhC0/gEgmIWHOPjhVYl/vHQM90vYJllaaKQC7fGSyXZnU5NxMZclKG4KpSxKOkrcUxKCrWnsUajX+wBDlidUCOciNFBiWcZuWw5tlYSUFBotGxor5RmNrT0Esekxwo/xV1e2ENILDfx3Ql1tJx/7oXwF4KhnmMHEWW+ZssV4JEeViIpBgVLCeJykpnYBbhoNN1WUJEkZqhrptqk37FKOKhRa1KesBXDU/KNVqmbDC5hk1zM+6DJoUaMj0CzF00ZMW2nFsdOXLh8xXzcf70X//ys9uuPnM7Tte+8pXputeX0b8uXYX0hxMywRwJq+B5SAadCgplsqqXI0xcdKa4WKvpleqwNOdjEqlNapVKB5sMKBDDqy1ojgcCTVXrshusrhFCVqZ1qBkMeFx/lBBrvQMkCMQCYfll0rVwxQnf/MWF32RIoGcawsOiJxYEUenHh0X8xrmjpe85CWf/OQn6UeTMsouqGxZgMoMgAHDBs8rkm9bJaJgpZ+9rESineIAVfn6YXQH0Ky6KA39wJTXSb+kWu8pSCCV3ITiONmo9aN+8bxpywbPrx4cG0Jkk9zoSDUbzufT1b5OgT6QGYnJUiIdgkOU+7mwzqhUb8MrcIpD3e/Y0JsOauWZceR+BeEfcCxtjo21qJnq9FimXi0Uso0ghTmRhDb396Tr5b17h3KFHNSTfpmJrr5XqpcjUQlBEEFkKQXvS4JK4XyY9mcy3nV3jObKsw/b2e9T2ylvPvBmMduyIpnl2v0DtYnxA1OTc/QdpHwKq6TGj29EUyc5+0WyAciTjHiKPIRNK+3Iv4ayK3slakMQ9JHF1hSPc6IalWIKHnACZE3G0GY87/Zd47mx8cedO8ixzVVqJE0PRgoYVtlegnqXbUX5ycCw9j+09KIgzwcZBkYztXKn551/+kC5PLNvZB5/MspmOC6IY/3ShVxW7BUnUEbj5OKrsVaqS9msqQozja0RZmg3i/Iq5+WV1Gy1nsmkWYBUZsx7ajpdm9mY93ryMCJgxhQhd562I6yV9+0/mGPpmnTtiNjBwR/wgXL/+O7arvGZrkLXX77mpRdsTXfGzYC4tWyH13PGroPlAkuG6w0OYlhtZ0yIPyDrzwnBki/v9KJ/8WGYh14GzMDJx0BZtBrlqi7wM3lmmBGCKMi0zs7CxAS9YZ7ho3FaEm+E7DzHtDzp6Ogvih/xPH46xr/kZb84vNFvVzkqjbc8yIIxJoiUvbkUXel8L/vwVOVQcJZApPP9Y7NQwudGs2auJHVI71J/+DrXUg4gNVbGoSWYpktyfBbMwGKLDJb/grVsQgkYg7vvec97GFWK5nSsTLZtlwqNQduktRBRsKyF4MnXL5++rAyVJRxwLG1jfuCowZI0JX7S5vDgjm0eUIarVUyAIuBnZ+cR0r2dcoCS/Uxei+gkXcIKYqV4B6BwLCixZKYMEkEETAqEVj1ZbGmoxtk0s6S1eesAZ2chAw7Mz84hT8BdpYJ2S6JKCX/McSM9aB5JnlLJ+pNZOgdztWxY3d7DmRjylqLijxwi3Jauzo7An54vIoW1v6EJHdL4zSPyJgvJ3LJcxSu04kTImiU9qgDxEWUWwSpCmTVH3lDJYyHNxkIB6YbKhxizxkMoXezLE7TzDtEWHZdrKcMHAuNyHAoEB7KMNdSZgZekIOovUeUzACSazdIa7dgvUCk0a+WYKqu3eEsBhbONBYSXRyk0PlqRGoaq1orGE/KKtXomrHfKLljijFdbNw3yUe05MERAXR5cC3IFepmc+sz3/P0f/5i9Kl/+lCdjo2nMe7mCdNP4dUJDtqNc6ijVMlpY+jBMJLev2JJflauWsille7bvinu7kfcUjopTdsRfur2Uq8SiZqlnac78ZHtRbE82s98MXhF7qERiSARNk7RIOU5RfY/90pwK9/KFLhAcpW+VArTiBIaDDIvmqO5cWEmHeeZn0AvE8KDBuFhV46kiJ2rIx06RC7nCHIia1smnymc3MTFhk4NYB/yIRzwCgPn5z3+OwQrlmJEJHJYrQ18bGz75TNdjCto+OehJaMcaf0gRorZK++AuwyBbKMc20PQQiKNjE/DQ1iDxVtBUnYoNkRDNTR1BjqAUIzazMDSYSQJS5hgIyCB3HWKWdEipt6cbuJ8cnxAQF2kTJR6I5U3UOJEqXOWHZZGlRiRfT/nSB2c8j32/GRLcvHGT7C2ipBCfpIi9acMGJvqOTUxCBmkDyqQRJUjkCP8i2SCvcAIDyDh7WMVrXMrlshAZpXCYkj7FgSE5nmvj5o1ovYJnJmWVQjW0L5eC+pGF/UzodRbkZKG54vzSCHR8YMxxFpngQmZzrIiDC8lrGSmBuOil2NSjouOjP8Y8VCxLcuJDiLliiZkInV3d1nMiE+TFWTsGUf7v3n2AAJJKrcq6X/5OVwMsHLfcckulOP2UJz4izco0hob1MxYiZferQmOyUaxYx0NYot+dkrVqFymMssduhI5mXi3ka/0eK9OC78Kdmpug2NLhS2DBbUnP9FwIo3d8+1beJf6r8WjChJRjquSGtsbVVlLYZA43CWs1mL8iaa4YAIOpoC/nA6LvXnfddTYhC9CFSlH10unkI7Bh4BWhfh0lQpMQmJQWI42FXThgi01Ga5aPzSUiHFvwItxlZpS6YXbhaNQ2DwwQRdoYDR2jpfyJHRIcGaKOrAgGYLPDJRtURaAhkIwBTaLks6JJk479Ngz0EXliapwAlgbShlcmmSSChuOV6UwMKpMYUIQPOzbMTE9u7s329SB3JSSeJE4Ingb7u5lJPD42IfQacXZNHiJMbnoreZEAzq4WYeWv5CMuoSR6Vk+5539AlwL+7d0/BpnbtmwSL/3RvTDimM6ms2eb0tEEkktUBpWXvV3dsHRyZtqYLJnwWh4SIpJ4x39DGpKi1VXCO8vKUotzaQpJhYP82BxSUtvSCeAlcaZYvc1RSD19MiLO1yBbb3pnbObDqw2NT/NRZuteXrLjPeqWd9V1nAZdPf/0bZtYihZ62YJ0qvhC2POKUHS+63un5/hW/IKYbdfUNXNgacZUK17KNgrJmmaB2WUjaBgpFJKN7iENcyGYKMd12xIn7n8uzcjyOMR3xTwYKeETox9MitYppJMt/e/F64DXmPUrVrxTMaFIMpx80WzEF/RlJJjUmJDFWYGs+sUf9AWPuTLzk1ey8hUZf391KoQBYDk5yhYBS4c5crAl1oHU/prXvSJLFZlmxW9kfJK2tGGwH0FgP4tnGCmCPMJ38SYAyQHATPsqkZs8MeVZAXhW1C/gnzDiFEgG+/sgick1SdLIESUIAkVjQkhjmmYQCUwVGSQ/hI5YtyanRTtko2AszEyusUKQODsoY8TtZogwbMzMyUbT9pMsIoccs7TRwMiBe3OURX/x82r8jVgEwWTLVXlORtwCPHRtjBX8xWf3/n2s1Nq2bRuxeOQKX2QjxsSJeReGLXaKNBIY75T0lfp6e0lXZ0GLpw4fKMfE8nAijjQ19aa4khkuYqw9LHOVmFBsbGeuGVUqSjCE8IbIU9PztTDo6OkTewTdKda3cCRDB2u7w7laQDvn8Cu+J+zpfBJoyd/84c8wevzqFY+lnFJUOmiaFLGA4b7uLiy3qP4ywCFdOzWESMJr5qQJxB27BaZRUjGhMzAtA/rxhIClRPFBwB9xxZCvnTaTqtQbcsaIFEd+NB8LoB0Yu121q31XwkZx9qdeYfocQ0vyVdocfgCY2mXXcSgzCWDho6vV6yIv97CmHDhEXpxo7nx8qL/ExuZ87733/sZv/AYnMTzpSU/ivF7Gfd///vf/xV/8Bcci2Te6f//+E81nHcezsoNqNHgD4I6c6g9RI2oqmjYXvHO6dTAdbTyQcSNjU34j3Dw4IA2dVCRY0oYUew2ANTpBqF0B4KaV+CZbWRjG21w+owJJghFjY38fc3BGJ6fISNKX6PKG7jT6K3eHOjyxRRN+/4Fp+lRbN25AzkpcNTxCv5SsIWtXcplUsVqbZUQ1TlxTU/RVAAaGQV/NRSaMRBJFA63qZaH3Q65SMwtOu0qizKiA9vYNDbEGadu2rVJGFcUwm1JbtUbR4qpcwi4e5Y2+lSP5fH9qehq+4S8nWeHfJL4XKDjaHSQvotjCRyyO3ggzxSnwLLKWaABlO5kDw6EodRKUv5SPH3N9WfNW6IxGu3N+HV22x/cGuwvVIHf3MB0yUbbgAPEOlL3bdh1MN8JfufysAiky2a/ONOnQJmfBNE7qrjbqMwbA0Scm2a2eg69wmKJFWQj/m2pG+1u8g3gKS3Xw065CFCxmnfhKDcEXdZUK1nXZoAaPEvf6VesbS1wGfSzkal+tN2+5kCWTb+iLo+qID88YIbKLmr/6QWTMmNWmz6V/NA40fY5HC3rk98zAAl8JMzQ09L73ve+uu+6SQcFsFoUY09OFF174hS98gbdmTUWNOHJqp/ZbEEamL8p6eXSDyKnOIGqDtA51NCOWBjGeUyzLkcBIh+GxSQCV5ZUy5kYwQitaLdvj5r2kxBgwOqvaoKT5SQbefLlE0zUNmDAkxXfQ29OJqjc6zbFGkTDCW48vxDIZoSOBkaQQxk9oUADAFLlr7550yt+5RcCJQV8y4Ubklco45uMw8FkP2RFaXi11omATTt6ohJMBV3kQ6uOr3q7WJcmZG7lXR+6UBMTSSdow/8DQEArctq1y1pOVy9QPs/1EsRb/sdYlKcUJUzTsAYDhdHFOxb3UoOXJpqGLYx/rkzKtKbB0IyI/1DR+Ng8fIvjGdKKRcdZqD2IEfcUr6lZIZH6QN1MspoIMi/ahjAAshqXsGDlO2zxY9TO37ZrOgbRMTUqlGde98gf7S7XUpRecxxJ1Po8gwB4Kolf5xOWRMeB8od4I5/V8a/XQNJsIX43bRTYVoWIZZ43LPnt4RVkX1YQ8RPzkjicsvXwJlJqyz5c9ztvmtYSS/o1kIb0auYmy45X8hP8r5g7FeIiwZm4asJDB/Cv5WJnYLtMwF8qwYlS4hE6WA9EncrLJ6Lx863lxDMPHP/7xT33qU6jCdBI5bo9B38c//vHsRgnqYHQVI+f924lMlzaLeqoyPG6X2kK0RtSHRpxm8aGgNdOWxcQ3OVdhYV9vp5yggFBLnMLc8s0boKWtkp2amVU2Cx6zuRXVJWmQHxjPXRdTJplJV6RPz5qTqLsfwyIDY2J+Bokta5bOZNlHUPGfKdpsspEJgsHeLt6SIrIb4kMZea4FfhUh0F3IozJMzBxGChBapZ7IKf3BCuGB+K+yS7JcnE+cc7STNgyZnJ6ucnxkn9r3Yr4RKRaFoKolob2oxanBFqlXDcDR9nC1XGFPZfVgiB5uI51PFIAXZ6VPKuu1SycMpxr4frjyY7CWH1Ch4ezKrdj/EwKM+TCmUg3p8DHuC+GMLEA23wBK8JbBLiZn7RmZ1JCZepClN/H9H1+byeQe/+jL+ZYa9BgFjFjxJuvOKD6/fIYh47CkxyMr4McAtUwBVsoLAqXvCPutnq0KJHWzu+hnJp+pfvN8+dwTmAiHOuMPr2SQJQwY+OdSrcmqIzwsJ421dhrwEiJZ/qRmJBkLgSIItt6wnM+gJbVyqShIqn5JGu5xTTmwYtXACmDMqkY7QAv0MgEPfZexLkAXQzTi3qbk0aTXtIhtkxlNFlo4XxbLL2yBRXBmMXVxy6cBlUuwqauDBb312bl52s/InIfMrldrW/tjsSHtHtFNuovrkejaBEmcrbm5skSk4sm2Caw0YvOA6ZkZKotDMiwgK10RPKdvwD5RnCnV51SamIgWmqWvQO+aPQeAIdYGz4WVWT3MtcyAEyRwmPGe/fvKleKDH3AhDV3EmfQwvE7Zw7/ODGKI2751W6XmHUQF5m0j0v1kA2HSF0WT7QKImJ4ulgwqpGSoT3EpFnNpRZ+MBSRJloudjPOJCV+Ouf3lrcOpTA7LDZtpUqocEBYHtn6MPqsyGfsTS8LQxaJoqljxRMlZGtvd0ZnOZe+8d5YA2VwOjpE5u6KJznKcjggSx6jhumBFl29pQvcWJVPrVHEDe4uWC4GxOsfz+xiKzmZzJNaoNGRlMidGl0g3qNbKDPrySL9DhxUZ/6ycfdoWOnAj47MEIcFaOnP3nlk+6Y5s6vIH9JNKNqPKNlWakWl6/KhzJmlS6n1Dw6SGIn6cBT3Z4JppE69IT4pSpZMB/ynFF7/7y3/41Fc5rynSYjVDWQBujo04tcMKD5nAHmTSM/NzqXR2cmomW5CTnqkFdoEkLGCXDnSpVRzV/kLACjoWFKqYjZIkYxYfTE/KEFV3V5cwV3Zx93t7vGJpDmCWQSxxzDGLCDn+b81ScNeV5MBiwX0SKfPZmUGVxUgM/X7mM5/B+Mwmdtr38t70pjfhiSpMDlik9YDok8hs/Uc1DRjZnVQA3VJVZNWD1qIKVYZW1UCTlPWGU0WOEPZ7Oruw6bGXkCw1wC2o0ERMEjOJLC0NmOeq2clsKbONWQ8JjUTe6XIiXTokaF0LUjM1wQkC21tBBdQFZrXK/huIocBHA4ei+SKHk+M1BqLPzuCNpov2A+1KlIhd2jtDZ6TTwdoddvzQ0290ITTvCI0nmz7KdKeKjiWzY6UJCnJnQ65a1KPTwKt9ifi5kA1HZRhMQMzszDzqRF93L6HsR6GEP5GLby2R2Ff+KiIGnMkce8IWFuTQtyha8VR/go0n6yRrKFX6vKBUFQ2bzV6G2bW42JitCcbATlogdg5qhVrFMJnKsysGJEonS6PzMkqI+VV8LZguqFPWnEE/PSSIZYrPxr4u+l5333MfQa1f9q0f/LQ0P/ekx15mBmf5CmQHGkmViBQZ/EaXpsPFgYR8XQIORqnku6ouQh7y4E4Iijyk3OgErNXDc7ru/fDaG390/c2T0/N8j8oNoSqaLQooS6zIW8dzWObDRmZ+NL9JtWxtvzBWGCguzjl+lvixn4VY4SvNnLzYtVs/NrKS3OhC8ocObpx7TM6qkrLCJTtlk6NxrZgDVkFc9Kq//du/veKKK2688UamZb3rXe/65je/icT/xS9+gcK3Z8+eHTt2EGzFcl0/CdGI+fZNwMEQuia5rB4pG7cEWkgipikW7YZZ0AQrc3gvg+vjzGb0Th+UXaCjNiQR4+Z0GD6A8SJwm9YhIGCLxTKezfo3cE6yfT1dByeKYzMsHIqTEw1YieLCRpX1eoahL8hEleaUwSCD/B2fwcM/bce2Db0yMDzDnK+Gz+wyjVZHUOHZ392DwJucZXesjez+Ua2UGFxEo0Lws8USWSM5JorsCybCgh9RMrw/SuFiIk/+r2UUZwcB1IUe1CPzxtnXYGxsAtDYuHEjhdJyHcp38xZS4mTkHus/lltEIp5SXfq2v6tn//gIR18E3oAJbBGOMt9Lbcca7CQu0m/wxd7r7R2deuNb/6qrZ+DZz3n+wx9yUUGnHPAtMc6P3l0QE4U4co/uII9Z0ErFvPSWvYGejgiAfcFv8DfvVbdt2uA3qqMjQySFVWSs6l1z8x3d6fqTLj+THJQRJLngKDudGTromGvmI9u7MEls2s3MWoixsnfCeKtTaBM+84C5TjqFcmAztNI7uWP3gVqxODo5vbknBzATjMAReXQ9+RaJJTXkFSmCKBZiZJ/TlUgEk5daFnmzFoWSmrJy8ccy1GYeyuYBwj9olb9yrrho53TbxRc6409Y7pxrLQcWpMZJ0sHXAKyi43IDxLIe/3GPe9yLX/xijM+veMUrWJKEAQqE5pUpfyeZ3XqMLi1TWqk0fwCY+wgCtbloq22qDgmFqsRS3YYtQzo4Nt8I0hsHZaZxBFOEEImiopv7xc7aZJ6FgKgdFXSeOFLgYT0j9Y6OvMYQ8xnvSHagrxfyhtklUZu0KAI2QUcEjF8Pco1UV8nLlb0MG1vWvAxbQFOMe3YPMz7ay44rRCNdksCobGJOZHudogwwxdpPjY1PyntM1DrfB7or9ErSIsRv3TP1g2tv4gZBwY9ga4q+xizNl6yXOF5i16H6BgY28IrHOPiSgMs8qsQW+KFIBkrcshKJYTnMBhKBiUkKgRZymSSO10vBgDQPTMxMlyqpfNeBsZkP/dMn3/W+T/34F8MTDcFR9itMsRZX9TYqI5bIMqVbvyjJcmamBML0deaR22mdLA2i69bXjU39mGFqHfnsvkn5AL7+gz2sSrrknB2n90nPaRnm6Medz2HeCOYrJeoXVsi3vgKK/1G5Y1yPgi16oNz1aNtz+hzlMNMzuHlmluGeyPJBHNMlDcksCcim/6pL1EDukFPFSJOUcKYuC6sE+aR80QstrzyuqKOdLUlPpEojzHMGi7yQshKCfg+fLvMxIHJR8ZdEdo+t4IB0jlbKMc3qjDPOAF/5DoDbV73qVdS9HYVk22CZ4suelAwYr1Sm6y4dWifmoOZ1wEkrXdxCBBc78jmamQ2uD49z2m6OldY0KoVGbU9MOZYet8L1crywcQFSSBorYkINqqEND2u7REuTUwE2DPT7uw4ODx/0zztLEoMgMZ1KK65xjJWXAiBnxNPrQAdOifxE/k7PzjNTdmCwr87rNHtLiyBSEUAS6L2yQ8HGgX5ejIyPqQhoYK9mSY9EF2T3APwPf+LfJqamtp1x1kU7e+2jbHAeEcVij4uE9OUKeJJ+UeeFQjXlApER/TLfRtBzamIaS+VAX7+GSuRYXMojECFSMoZEBTyeWS9A7U5OT0lq2i07QgJHfqX0WE0lVImche50vmN07/BMubHt9LOr9eCWe/fe+/FPn3fRub/2lCc8eIdsTA5dWgBZjbYgnOOH6Zk5PLvzIs2zDB0yHsysKh7CoJD2N/d1DBf9XfvmNvZ1fu9nt5fC9OMe9gCsHIxqSJCInZoWMXmW7mYHVv35kkwVkQB8tvyJg0qI1XCSvnDGeBKxi2fmB4vpPUA1LGe8A2Ps5ub3eNn5SjXNJl6E59Oj3oiOky8aDog/d+wlQqOjYYK1HMxoQUif8JKLxLF8CCtZr6rT1h/lUC1z3EmDDXaSHKGDxQ7lagloDmXcgzYrnQrIWrsObkKNuzmEAysJwKCvAS0zn1n7e/XVV4+MjJAjU7GQOJhHUIgxSrNHh32+hxBzinsoUkoZsbsaphYKC01laeHB2LDRWcjTysssdMAEPToJhsE9adymOgAPAsC8pE0t39Y58RBJYQq3hUGY2PEYALDIDtmnkuhiI+3v62JEbGx0mA0HoxQlM/TZDJthguEYnMFptOnJqje01zuwb2i+NPezX/4ync1u375VewHMtkFSy96/QiSDh1hwQ29woBu9GA1YhCG6tK4fJeO6bpx1977GHbsO1OqVXfuHLtjZK0QJTQyL012JSofnijtysdQFJXlQF/+VBwzIouuoBkzPcrA/3rpEQx4reJCi5BQ2ZE2T3A709qGQTM0saMB4npSDS5aEUsstk+k6Cyk2ypiaLz3xIY94+tMf/P2r9/3DRz+y/4c/3btr90PPOf+Jlz/i0vO6yZR1QR1ylJF8A1K77OGspEypgt6Zk4MG2byat1SrvFJkOvO0LXtuuHNianr3bOfeqaqfyj3kvNP7ECcQIgyTyte/aNWanMwHpDeVLcu0J0Wo2D96vSp/dIDbCNH0yZMiKMDqXGDMMtihZSH7rJ/OzlaAsCosJJjutKFWo6ixSXwjGRVCWiersmqhNSXS5GeNOQJgCyrNZ7WcacAqVSQbMqSZ85jNSXWKo4PgB6IBY2qqynJlHWiiZpxrFw6sGADL+L/vy5YRYfjtb3+bA5H4NNGDGRLevXs3E6/4OF70oheBHyBxNHG0XZiwRnRoU5G8AGCYIE1l8RhwJLKMHHASa5JMjJUd7/AbHh9nXtLGwUHkhZk0BZxodjSzqGtuMRddjdUJABOcxmkjBcC/NFT+K3Lyt0+OXm9MTY6Rnryyt6yE0q79tXce+Py3fnTzXXtmi41U0NGZ6ewp5GbH95ZqpZ6dZzLPGbiCHiHP4tLSWfOCa3icNwyaygHvIqnkvEPSZ6Ewwg4Yvu7mW7sHNtUb1aGxCdQjs4zrqJvaLi211bnCEAgSgoUPct/smGsqHQCGOcfG/bBv4yaV5dLjicLLjfnpnV0i1iU+sIUN8nWkAObw1kxBth00/iRnv4hvScRjulFiDgnZkRdzBZ22zt4BJs7x/kmP3X7FY9/2kU//4O5bf/GVb1w5Nz6+ofd52zan9PDJReqQqXDzcyX40ZWXY45kibAvi31xOku4cfr2LdWf3jw0PLLv6upMLXX5JQ/e1Me5g/TQMG2D4zI+qpSJ8ojjE2PROxOCipWaLE9jizTq1t5pgNW6UME4rRL7+KOMWNnHXGXdOhL/AyOjXX2D9dmx2aIOfVNii0hoqo/5i/ponLH2SIeSAqLQW7KE0g9BGmyUxSr/MQBOMiFbpAp81lkaiXd0JLDuChyx2wiWENB8Qt/cQuru7uQ4sGIAbLNt7UCk173uddif/+qv/kpm1cZwKwLI980iDVpb+JMjvo1jS4OnIy1/+M+Hz09mwooqweipNuqwjr1OGqtKBy76Ny6UPDcyso+VP1eTuU7MKg9r84y+UWci1Wg8GifePkGb/+JUaFwZrIaMuDZUFUWsEA9Ftl6jk9yRkUnLkKSzoGT0rjMnpxPOMv9VCTbls56SZUrTnnfTHfvvuHt3tRJu6O5noUVvIbdxsHvzAy5jLvOWM8+4/JxcAdXGl5NYrFCUXp6QxT52MLC4VisV6wjduhx3zxriVChK1azn3bxrdGq+Rk99/8g4PkSX8mE25VbWPK+Wg1tR6gIFgixCueYOvwSOWezRSCOSx+fKqXx5A3tqLtBClKM5YTeab93PpFgJpuPsoidxuh+9pmLFByOZU04qpMUaa3zJgEhxLrIHtTkZwBcnAXCqhcqNsUr+ir8OXmoAyoXR4p79I362q5/Jdfoea+n/fMljdg095rOf+twPf3bzxk2b/scLfqWXupFReEYE0tQVRgfKzofBUG3VL+Q4z0icHK3AbB7SDjlW0Qt6erpz2cz+4bFdw3dV5spP/JWnyDepXRPCQIfNmycm35uw1Zd5exmAHj7QANYMgJX65ktUbbJFBcyUSoG64dHJVLbAXHGm9sPzpGr5dOUjgHopOUBcDVKZciOoBFks0D7z9HWxGuyiYdMthu0UTpzWgtxoneIfr0QTv2N0mucCMZompNHyxVkO8Bb6+UE2FjHErE171tfMnwyynIIGEbK7GTVAXXCACi1LP/yESEvRXdecA9TIijkmqmB8Jrl9+/YBwPQTAWA0MPMHfcFdG5I8VdGX79mabiQf9fvWL13aDKxAcrHIh7a/d9++Dr+GIJYXxKFti9KgjlgpdhES+xEB/EZqrNYziRI2OdKXqVx0WoZZVYQUyGAFLjCWzvNo+Uqri+74Sw/fGyz41dnxaj0PivBIBAZc90/OpL3UNqSyCBvG98T+nKmFZ27ePF9LzzSyhOlEOpFUuRF2sDKYZcTBN793HSO8b3717z3sAjGdI6aZw23ihmTxkfPlOOVQcyFBQZawQAmxM3O7sTM9O9u4b2imc0tPnm13wypHE1dSHdi7r759pCcAByo33XJH2ntaxAdGf6FLei3Gl1W4grKintJJYStgMkMcUyFkjJxixXPVS3VxguDNI14137NjY6ZHi6Z8gZiY0UrX8jRKF6Yhq7ZQc3UyqlRYtXHmxlwhyN5xz+i0bIyVQ3dhRynRMuvVehpW6f7aeAhVUkXEEpQgMcS8sIQ/gUGy9vH4HuzwGyVFviWpDvpYk7XMeMk/Y2Nvn0CerAUijZ7N3h2XXXbb7pG9lYBNUqSWypVGrpuMMMt0ELE6Wcj1HZwqldIDA5t3SHX4hXq91J+WIf+aHwDXO87YwTYy19xwQyqTOa+//zFnyMzqjmxBgtDTZimsj/VDVp8K4TrYzLt81s+w3/hwydvJxiywEHIWsVELsIIXySOuL/mO5Cc0Sb2QDfuANgIWR3lDE5OzbKSc6xqfKRKGpmhmXMEutTdJIrVyKk3HMTNaaswF+R0D3TPzQxPjU8IfVr2n67qJdoNjkpNOpHR7SYCThqnDpIEfT/m0xoggwkESkyJQKO2riWrup1LSi6UIzDCfLlYZsWfbVzEjQTAd7zDdkapnG5XZmXJqS47vu4C5jMka8pajN1ZS/it17nJ8HFixrx/V1gZ6yf/Xfu3XbrrpJozPbHqFrmMGN9N6zSJqNpzjo3SdhNYGI2IFERlrVNba+fil0dMppbWgF6XCGgAqjYoWry7+i48qOqysBSeD9HyYO4hYC+vd+QBxKZGQIlSdiBMRMbRJuU1S0QcuCG5mq3J4XLUuM57wITtprphWwzqzlnW6pC7gZVJVGPYU8qVGMDlXhnRdIwy8IyOhPPjlfs6fL/R1dJ29sdAdep01r88vdnhTCHp+nRwoHxbtPHNBC81I5QVWPjoM4vo6stnAn5yvlUT0widji3fnfvaA6Okf2CAGkmJ1WrfygFTppUnpVtVROKkVkV+RgOMZ4tj5Cz6JFxDIlhSVINOV59x5Y7Lw/FjJkmJSX6JCwQfKlffLHRlZm2S6NWDGPRxTVQksVm5xEULkwTBY7oANriLm7aURI8E0D7kxp1VPvbN1Gp2IdE8+Sx+ti1Vt9Ka8GvXV099TCoJR1tAoSaC3fRvy2cm3V0FBLVXq5UY2lZEBATFmsPVio5GjGStJHR3MEEzTw+7pyF1+8emkSXePToy8pHugX6hxSWgjdX7aY6RSZV83PMW+kRRFfFbHkY3kDWX8iasNTsJKzOpijuJuniM/mY7lB1VgVOmgUiSEOU1AkglrpFBu+PVUNp9juU+9qhuZaeJ8QRW6V8SwUkk6ElHBsikxS/LYrlrjcdClzIpJhE77gGVwndanK5slb19WEGZk1bdWG++sSMzTkM/bChqn7v62ggMr1gMy4zMGZ0rxyEc+8o//+I9ZB3zxxRfTtJmGZ1MVnv3sZxsAa3tvRXHbIE8RdTpdgqvZA5Ynqg5KYrkUvbnRqMvpFY2QLo4FPnYGYo8CyRgD1mwltu2JQwpswyXEMBIrQ8rienp7GJydnJhGeRE/sYrSjtGcvZ/ceHMprJ5z2rbTBxUtJDmklUBHkrIkcahTWgkz2D8wOs6ZhFP1M/sVbMgjN1f3br5xHNF90Xln77prZvLAyMiEt2VAJQXDr7U6k6UPTXIlfaBMdYrFaarUVq4g2iYmZMDeNg5bHOw4nmC1MlPGQQuFjHAlrHM+VE/Ee5ICm5rGYuPOBwRKkAUuizIUeSiy26P6RRd8LNbk5DTA2dur25uIGK6JzTXwtmzsI3tmRAKnYmKQA32bHN0zz2eqUb1e7Yw3axOCWSHM2JEmPtjtdRWyk5PDXd2Dj3uk7L/Byu96g0XdlFLoXUIVYEEn0dbd6YY8YrZZCwcjDpsTdKZEYqW96enJapkpFz6TlQguWAtbmujDUz9k8WIGJd9HT2fHJPuGxdv/SRzpWfK9iGuOaz4rf5XWu8ip+G1kZPueBWf7D7KCn4qJPq+l8RYCu7s15sCKATB002LNtszBR0j897znPexOh7/Mg9dpREgxBjLpNR8JeNaYAWuenbUa+AM38rLG8jBOdFppxshrBnH27QWPw75eJkktCILDxFzkDX5TKfBfIuqbIpqqbFMX2IxeVvsklqjugpft6OLIVrFRqXm8IcN+7Cvo3XT7HcVq+fJLL2bnJEaQ2BJaLJ0c7Jo4EVHJg9zoE77ikAiDg/2NOxujE1OyTYBqemEmw8bTd976S68884gHPzksDu8anhoemQsGOlMIbFQl1fMthVW7RoABhYvIFyJFjvF3nMnbvr+hf6BZrh0rPVbfgFccAXWzM+/nsmmOhJ6e9rb0qeiWKVpkthAqyT0iLGKkBLD5u5YeVSGLadQEYj6akNwCBax0Sqez3d2URXwEfYF+6mIA44ePgYodXhgzYMdxeUs1WQn9NCgkcw7qte6uDvylNnjHqAgjiBoKY8CG7sKdt431nb753O0yuCiiRGY2SVKHc7b+EGsZBhHCGOuPFOFwCR2Xf5xBxMkorgKsrEkXRlHYeiNLm0AThqrECT/i6DJWoX0LBB3fw0B/756AHWNlzFi8je9x4CiFqNaS9Fb0hppETSeLOFOba9ksXQlij/Yqpor+lfT2VpQal9iJcMAa3InEXBKHzhfIalhLrxARb/ZnbujwYgHhq8WfMADKQrdxSSr3g0drLMYBFmboozZ5NXlxp212oXmkRA8O9u/dl00FfX09x8sh+r8w3DpAlrUeRRg1S8FBtcWRLDKFrHv7Btj2Z1bOJJTlRoyBIXf2jdQOjE1ms5mHX7qD0VtGtmi7lTDVCHKRhNHZN0pb9EVFMkEykL0WCbZhcAApMDrJmYQ4UINxZSY3eeNDezv8ynlnZnZu38Z83Xv2HiBxhL2kvAYyQghNRC7EG/2MEyCk5InL2Pg4/RXOSybocTeYSGNMhCRpCPewGMHwiQlswAK7amqMeKmZaMYSViIKjUYk85llbFWVYIIrjfJysbOvaGZe9DMywj4sqKvJW0+gM+11dxaqpTIfg3gzKVJTEJCVMgccMDA/NweY9nTZWISVnLB8EUI/10vPO/OCM7Y+4uILOrFzyq/OFtmHOElZ6ZdrZ2cByT83X4K7Ss4hwVfcQ3BRCsef6G4hY2EjnmzlVqtWaV+UnlnNfJ8WxCiXVqEOWwJJ8bZYLDFdbbCvn/hiuo5eGwvlIW5S9mLhmnxnC14rd0fihrLsr6epRnQjkylCBMCUFm5IHYqTUTHnWsqBhY/mJMlA02KRyROf+EQ6XFQ2iPvmN7+ZEwnZ/YrHu++++1nPehavTNybIfokc1yn0fn2+eyRjLAiNu8dtigEhlcg6NCB/Zl0auPCFpELUUgHt/C8+K5ZA+YN4ebnZZjKhgy4IXHxR+6qfYppdPhQcegFrD9C3BD6ZzffWa3Wzj5r5yCyll2AdGfjGvZIYh7i8FzwV8K4kC/EI9aZ7iwALMOZ2Cu9u3bNNcpz52zr35DztmwaBOzvuXd3kiSzoQ5bsCTQCt3AahrDQnvQLgW5Qy3cACwHBwZOJCuBvgVnD1x7ejgrzJucnFh4F1ei0KDhYCN/9QQqFfKitURDwjFbhNmEFwRXB9zEzA9Gx8eofVYAxukBrJI2AQDKjf293I+Oc44ATI7yjNXXVLnemJ+fYzpPd6fQIMywLJlkC5dCGfh//KMe9uLnPvPpT3oE8l4gmRl1UWG196BFUKIWLt26Aw8asJU16uMsvF/xO+lMNCcqaGqEgab6AbMOeN/BOcZNezoKfO/sOie7sxFK1Et1xNE7uagnGgVbxAz09nAtl8VmHYWUUmnfSONpxnFtLMcNS/4kr/FXI9XKKmaIRcwmufGY42wNOtN6UkTkr3QKqRHdJ0mCi37iHFj0dZ54MiLW52nq3/nOd9gPC62Lx3/+539G8WIGFo8ovldeeSVKsGWBODuZvNZpXGunlJymYoPli7uqTcVCjmrjEMVCD2QYPnggG4SD/f1LGHcE6LXkQFO0t6j/q1KUSiEWxkCRFiTXdDgVhHV1dHMqzsj4RLmOsVHk1WzDY5P6dOg/9qEPlXpD6S1PpTmaUPfQIMqRviFB8BBkR0CjQULMxDTDvurSWb6Ga278ZS6sPeqB5zPVZ+vGDWwQvXf/ATKFQjXtNfFktW5BH9EImn+SFRNE4ZvMKfLGpyYRq/09skUXwY7fRZIujgvPvN6ubrQoLJ/c4y/75yu+RsyEBXFoBWC+HYnFd4HZgJ/0oeQ7EvbK7WKHF1ry6Mg4Y66cCspL6WRhq9bUuQCfmzf2swf3gaERkmA3Cvnc9L3UThiwWUalVC5k0h3snqHVIZOzCCQ/GedFqO/o8x/3sLPpOXGfYxae5LGUGAohpZPc+S9jwHxUfIEy9nxoaAmyWg4ahAycZAy3ZbKZlJ1FwAeHAa3uLqYShhxiAW+VWAmrsaJCqQYsnU4sB5SCTWvYypyedKzNLwV7ib/6LpEA1Iws9vUZ2DFDhPRxyd/GgA2AI3Ls08LitbS6Vp9cl8NiDkTf5GLPE3lCo6JdIev5lIlPsweDWYDElW/Uxn6AYRGsOvx5Inms8zgLTUWEobQNMGz5CqCF0JbUZZkg06jPTE0ws6IPGXGcjhqhu6PL8CUmLc6wH1GYSBnaofWIMD4X0ikOQZgozZVVdSDMTOjdes8uQj/0gg1lMJNFKNU5WTOjSLCUHGvbTSKMAMA8wrmniyUT/kyxIr0wNaXO1L1b77g361cvPXcnH81gX46R0SnZAREJyAacCYFLM1mxZ6iNCVZRS3XEFWLKog4Qzs1zwESdLSkEYo7LySiyWNMX2fpU7BVILRBRTnoxCdzGueMVC0fmNmk3QDJmMzIiWLdAAFskLD+CLsOrmZm5TJYJ0PF0J60ynbQr2WzoZqA9YCsrehisa+JlnLcwnxkK7IuCJAeq6YWpKUQIUFpZwC0Tx+kdFvCoe9UiR2diMGk09LizqEBCv5GnEbVsCRjERtDo1Sr/kX6m/ZSrxqsAzZU7mMtUFaQW275S27W69hkhXatAY0l4/qu+jofgHB1kLPuYG1jeDQDrW0JEdQYnJVyzk3O9V9eRuTVz2m9SlWSJBOCKwDEC5CoNXkikjKtN1eqWef2n3lxTJ1saBhtQcYBe03T5rNm4GE8+7gR7aIE4g5+Tza/V8fnc7Ys3Qo5SKOY9sd5Uv/ypGTH8wofYVGTtV9o57UH+4Fg0LOe2eflCtl6rcjDw9Pj4GTv7l1QY4GpO4yxzodMD4sq0EX0JrDMjnepQXSTyMvRFqKKenLF5S61cnve8g7Ozsq+G533le3ekOnsfds65p3V4WZG41aCr4M2L6rYUkAQSIsnDnwo6LF2MsM6R5Tz2dsnxq6Njkyhw6Fysj9l1kINlqnm/fskZMiFsQ5cMTLJNw117p5lyli8sKatSu+KXJju6II3ihEilAPVGzjOfnvcmJifx37ZFej8nQhM7IGs0EhHH5tgyJW2QD2ZoaAhPRKP0xzjeTrfsjoNFwpEhu+ijUPS98ppdv/fGd33np78oFQW8JT37ZEyUqpnFIkxyFkOptH37dnoyYicVk0WNr4Vag4BtmzeS7K49+4gnGCwrziQ1diz0U9kRNqbwfPaxE2IYN2YRM1RTDJ0vzW7/Bebnqymbm25OWeLrQTOWMRVS4TOQpMzxLOCjpRoY6OdrpF+OuoyfziWIw63WXy2VJS5UqeoNMT47auhWVnVvcma2UiydfeYOv1HnfF9WAIPKUgzwibnD3GnfRcxSdWkrtKByaf6M07xsmr1k/Jl56RLBc750erIEEMCPSowvb1bH8dE0GrboiJIxVo+2gxqejvb2llqlLHxmjEQwFEi9i0DBZWVpGbEXAbW+cZc15kDT13nSOTPritYFJiH0uefgI45CAmnwpPo3bdqE3GecmEf9Sk86v1YnQNFwRgWlTjoZy9MlU5zYoUBemnK7ENki6KuFuPEjR6MxJZgmzb7QR9g6eiHi4ruk/xtlwlGALEnykahmp1oUmjw3AK718q6R0e5Bdm8Wa+dNd92DEeMh55zdS4MmuEgXFfMqdBbF1wfC8FuQOkh81SvZ0rCrI8/ZxgwC15De2cyd947wMZy9czPqLx8iGtW2TQNhrXpwfBJJASzhGbPh0HxWzkeVkygjKWHk6mxhhQGgKLu2dXd3IuCWdjjikIf9S6KLC2DtDb8eZhdj8CyKuo8jI3jAmm/l3IJ9VjmgvFRUAHKvv/3eiaJ3x56DWb4GSXyB05JG7FByivNMfpTBflZ7q6hlNpweo6fc7u4QS9XMrIwIRElo/wOcRRku1YCakDFgIdh4InnxpCXQkAbAslQtwhhw18A96YbF1CidXGyXRCod8po4HQdbhb/kEmdkdKLeR8Uts/EJfYiUnE1SrVW2bBhkwiPF5pu3KFJUCRwlwB8WrHEF0uidUHxM9PRL2DgOT3lLg6Fg6pRN3EWP5snD8rVlr0/6yjIzcmDZg/BbHTeLJIBadOSzOaFdQeJU3d8V48AyUviE0zYL8ytf+UqqnG+Uo3/f/va3AzOoX4ATkAwMY4teVvSfcKatiogQSQoiDTJG4iPTYw2DOWoEI3rSTppjaZMVSQdyEYATCWVTrLAx0M85pc0Bj+neiEw0dUSE2SfQgKP4pp2J+BDhukkEc3jv0BCynt+uA9W7d+3NZdKXXbQTK3QlYBtBX+iGOpFE3ByWjKibxRkEdMtF+HIMX88YB67OeTMdHmW56eZbauXSQy5+ENCLDkGY804/7dZf3HTXnqHHXLoT9USs4PiuqovTl3Io60WKSo5yABC/MeZJpQLWIFHqOOzxEASgqYraJHnllm3SG2Gd/ih5SY74kbp8RbKLhbiFzOw5YAH3LBPibts152Vv3bWPOepZ4T4x+UV9A0wNPBABdW1qZprVn6wd553VBT0NEJjs8NnQ1wUwj01MGgBzarSmI8orBtaZOTbqCOTbU1r0srimhW7tKESYwtsogHacJIY9SwpCkPjYV6ezEJoLKK9W3QkBxtuoImWSAUCqk+wY8D7j9D66G8wH53zBPiVdy040OMp/3XksnRF2q+4ID8UuNVPm4OQokHyvkg3/m/gWlUxSWVknVRblY38QSlCwpGudycqJ4CYBJEbsuF95kuLE3d9j5IC1kWMMfKRg4CvW5j/8wz/kSmWza8Fv//ZvY45mEha6L3YwHpEFhgfInSOltR7eWb8SSvnoAWAj+chKMG/5/mmcDIoTHst83ByWUwd0qS7BkFlEbNSq7Oh7/PgbTcGQcXclkb4xE39okDYqL57JXAyVS4NdGYY6J2bLyHoGa2+5e3el2jh7x44zNsr+iDWPRRoK1Clm8HgcVswHpKWw5BOBIJlJCLxVJyAYT7293fRURidld66xee++3btROR5yyUW8tXTO2bEZ/fvefQdhUNQxiBNW8lf8IuKUH7kvcWRre4CwBon6ZXI4xaQIcZUtCX5cj/IZDPR10UxoCKAATkoZJQ098sT/6KuS90Cd2I6HZr2hGfYV7R+eqdA5sJDyF7eYMp5okmRBm7P3kqJ2E60uNvR101THJiYgQJCICGpopSYIiCWWL7ybicFRZE2fB/uJZxP6ik0dKNPfEjqUvZqGnGTY1cmYslhKbeLS4jJqqBW9LE5f2RlrtNSBaIv6G5ugimunb9etOsNwriz+OMbBpTSq38dNPChx5G6tnk0JzPV2spYsNc0W0uaSvmz0vMp/muFUs7K5lnnh8YKzSTmJBJAX8gHqzMOFUO6uNRyIG+dJ5w7iksYHPvAB2m2k+mia9HYNktl2Bw8GpUAUfPTlOr4YABvi2j3CDmQ9wgmDElh7naoByFFIlJ+msJwTRLCOuigNjVpYrw7qgNxhwi+XhvpZLvQSeEIeER2hTwXZ5FgzmCFcTHQwu3Vrv8f812JJj3/wvB/d9Iucl7nsAZewzxEDicjYENsbCSFxdS9ozWT5i8zsFRFI6tGhfj1dTLGW8cX6zr5b7xudK5Y2Dw7u3JJF4BGWHLYN9qDv7x2ZRvkW8cLGDoIZwo3VdmKH1nJhZJQ5NpK/KD3jkxNwr7evR6QxhEDVcblIn47iJLF7euBMnXFcxlwBL/gvX4fyyshoykThgLHxtH/LPV4xVUj56dlqFRZ1CGeITwAlXeNYObhyoK/vd5ARmcoACCMfMFnupCfRLSPuXSNjUyzF7mB0m+LGaZDc5PQMA9cyMdjcwkt5lqoR0AWDtWqEL+I0gRiqtJ9m/sk7ViFBgM5CWHiz2ncUR+zkQp11GiRDnukg4jVXY0y3WMjl+2SAm+nfwSxrowsapl5lhFXJi1jDH1Zy0CHjzF3lITXgY8YHiyUEKeq8DY2iX20UD5ZFLLJXK3aVmtAurlaQ9uyZBqtZM34i1S37ICF5DJutMEnuypnkyd20gAMr+VkwtwL9hq/TymFVzlQjHvHfuHEjPmaqNVRoQXFXNEtKypeNTm8ATNEwwh81B5okAEywQ5kQMc6S0KbFLZPY1KPRP9C7pP1YwCNfjf9WF4QkBdsWNNKAVUAg+q3SwJ7BLnbnq5Qr0u0f9bwbbr+7M5N7yAU7NZdS1qfnlOLoglBNlmzKccgHJFsRmlNqEXkghKIw5w2zk1fojYzJEO8Nt9wJ9y696HwEWYPjwlVAbujJ9XYV2BB/LFIqYntslOQq/4EbscQkY11u441OjDMYPNDXn7w6YSKS6oNpnYjuIEBiYg3h0WpbzqzBxYOUTRnJKC7vrr/51qqfK3mZ2WpjfBqJ39yBThgv8Ti5korm42QZsEJ8lDm9Iuwt8DwfsOlKD2U8OCQH6onDQqODuzxOTs/SJ+ju7Ejql9STnwWPrjG00GvRvkASY1Eo0yN1GbBowLGQWBxmtZ+knFoIhStGf3kYHZPJw3aKDLBKbzGe3Cb+SjbdQ5zUP99EqSSr+HI5kWuMr/NiZm42YiABkjq2skhV8jsMT1a0vGRjzTxa3BgnvqQLLqTKfy1PHMb9bRUHVvLLMCWYr9P0La5IF1CK7xjIkW/Y98ED829VgVcqX8pFQSiRJYhMsbHVI6QPZ3jLpblrsiQ8IWhIEk4dN0zCSjNhM53u7+07gdpiGw9SogqSNGVGtGwELWPASpFc7YZWSej+Hg5vro953k/vLk5WKju3bDtri1KTkbcAMOc5NVKc8iBIsUTgaDihP170oJqkSiCgmzEIys7SFyzMv7zjHuzqlz3iwURh9RFXkuLYvMG+7tly/QDgj5NuAL8WODM/kDcaMOxi0rLMLaVgCR+PjahlgqsXU7xpC9RLKdlJiSzFELHceAR5qcn09nt211P5XFdfsRJOzXFkFi+W/yj4HLE2of10pWMORlUSsHCGeMLtnh4CHBwZiYqiHwHUUerZ+Tn6B0mH0krBNbnRKGStRg4+sAh9CbA8PZZFno28fL9I/84SijJexT8L+cjd0m8JPw4iDFIpzimH7kJBGkVJj+OU0DIJjzJLTJvXRL9hvlziEVSjLbCnLjaAYlH2eV3ISOIs4whw1DDLRDtmLxJXwdtYMlOEzwye82ohdy2RdSmOOXkXcFU4cKTWclwZAkhUM1c63VyJSwPmM+URf+uFGfAAxvZ4XOm3LjBlkR+fL80R5EB7pQkyR6WUzTKaOuN5e2e9G3aPX3/38C0HpubpQctPZhURmL1xOSKG3Rv1l7WEhFceOmTSXTZpL9LXflJY2XFQXD7VKASVjiDs6iwsNCF9dSwXJtqg1sholrZ/0pytB5iSCyk5NzAQESP5WF40TGambNi0sRZmd414P/r5XdnugXN2busGH9m3yq+hMzDiXfdyFAcRjvITE6wPKnzxEVywFEU94ifHLpFGb16065lKY3/o3Ts6xwLSB57lleelH0MgVIpuwnR3MAV3bEYYqGJPoh+rg0H6s8pCi5Zi48xfH80Tj9gTujQLvAyX9K/uRRnMYsidDwt+bUOnLq4koJTtRBxR45KITagD8R3UymFmusZpVyrkIUAs0WQg5ng+EUS89nio9zzcmC17E9OT+Wx6c3dnUKvPVqocbakreYiFpcE4LgRS6vFqZibMcEZeP8w3emX9Edt6cw5WNObNytdUNjMxhRXWyi4pEB1C2JCREd0lotySWXQlNL/YUUC+EClm7IkdWiBZgklROiGmUWNeeUkD4BXzJE5iRf+SidWuHNMkh3Pi6DNSsgBuKW85hmGaZVfdvT0E7sjKrtgcMcI9b2WjCu5kzbp0tPkxD67EWLbfyKdCksuwfppzK6tVSsEPe1CN4xblPiRfsfXjgmw5yM0wsVxlAlXDT9/AbX7w3n72qFGSi1j4hUNqohLGyoM4uGoUybFajJTgj7SpULYwjPYO5TTQQIjJSrMt0wEnG6NI/+q4ffxsibrr2nMgapsnn7GZYe1q+JoM9JonWViH2oyfgBD+zBMxvdnODDYy6LkD1eyrhVqJkYeQ6I7oCiTLDVq1pYyPCG7OTLUBzpAtYKSNEWDlAJ5WwaG1lUaQDtMFvmCDVUxObImwa7937fW/+NmNNw6PjPnpFG2iVprfvrH7wnNOf+iDHnzumVv6O+VUUWiiedBcSroslpmWs0wx9euDPbJjjTQBWdFARiIskhbBPge0KLTGTV2Znsbs0OTkBeeyU+NCAOPVUa+MtnfkMxOTY9AA/aRw1/BcI9eztS9PBlkmdTZq6Niko6elCJ1eIZ8qpHcd8G66ddSv5R/9qPPkbFQ6FXKEWT0dZBiYFUGAJixGSw4cpAh4iB9XkVz2R6iVlKklKeFc+fzTBgu59IHpypW3eUN+99MvGegnJMKMtzqiXK555+zY8eO9u268/c5nnntuYZGJVZM/6oVigF4pObOWZMlXFnsKd4VAJonCBMgTSzrdRGQaRbH+D7447gX5CFWH/ySye6ycmR+5YHOGqescgkNtWkANfdQLMCp9AE5eNdVQatDHzildjR392Z/Nd+4qeqcr1urwIaEgmcUwknJKGO7P+3JkHgy+5oZdnAA4MNh7Rn82U/DuuHfvJaefx2mDWcnCSsi6VKGP/t+ds9np9MD53d4gsKeeNA8zg+Qz2VK5FOTyZ51xxlXX3nz3voM17zySYNC3USv7GTnOdmqmxFSALQP9Qojyx2pXyBIP/U+y0YPUPT+lmpTAKJktrKPAspsYLp2SufM9DPN354Ya6T3zXh8AEVZYEKdRNdCKX5izH1b9IDcnXRuvGwpYqxvQGwY7ZViEyt97YH+WqaK9A9Rsb0cWVo9Pz2a9gVQ1TBU6avWyn86DrJypSQrltDcyy8Fi5cFuMddv6OmYK89VamVp46E3Tz1KMatidAD1cJydnMrcVvP+4ZNXPujMs37t8WfjR8e3UpzXxdN1r4KsK2c6u1UIQB/JRE6ZqR+xTJiCi3xLOAauOXmYfdq5qeewQTVqmWq6nvPGSSyTT9dKFJMCVlMybYMvoT9by1ZnZopFkoAmvnyZV1GvsMaCvdhWkflROdyfI3GAr781DsTFrGfoCwU2BgPiMpKKdRQHjoK+vAJiwWDDVBAX9E0s22A56IuSbWUwSF459CVVkWupLPqbwCGf9Q13HrzhlnsOTMz+8LobwkyBwwOQ94XuAdtxtzw/MzI3tf+nN3/zBz/v6ujcedq2c846+/Qd27EeX3xurqKDubSKsblyGKQK+U4+f5yKR7u1NiaNEFmJMkpHOkNTmZ/oz6eZDgIBx+sQMTKpSI1pZE0KpZB+cb2Q9jmYNtGcKCeNU0qrI1t1b+rnN+5FCl+07bQtG4Ex5JKIAAsCeRJSNvLDDE2qi2Qozyp7NC2fAxsQFWLAywWNzqxXrZSGx6duurdW8vLnnbmZLQxrMvLJ2TwivrHrbRzobzTunZicJj+d9mTGP03tqBco4ycXAF0GTfUp+SOPeMJrAkTv7Jm8cVYJEkQC8n+GKbsVf2PG6+Mc9nhHjIXSaaSjXUgNx9wnTT2uRGydPTnwJzvKxmJREhBBnnQI5AOQXHTNF6ynH8DjgdHhemX+nO0bNuXruxuViZkZqxKNrcXRUoDBfEbDRa/qFwYKQZeYFji1Ft1OJ75qL5D1uFReP6dPVqtTc2IEh8qkXNyX2QqrWuluUoGNQxGl9qfJK76NFfHomT9SFn7WpaDUBVA3yMzqpyxbdMYxF6W8gg/YfsIGIEySCsDSEvjJJy2Zp1kUUK5UGRwBRDsyUBswfC5EmUUB460cPiIM4ksHe+UcCb+aS4esYWDohBX65VoV1vG1YTQgJOlIxbHHmWxbUweAbx3yrr93aO89+++5/bYXPfsJ52wpZAsdk/PznJDt5fKZXLY8N5fr7E2+v4glkqvyTp9Vg+VZCsIb4Z8UCoajhPPA4Sh0EFPoK1aPymDxz/r1dFhmGiS1vNjBGRLBT7/Mxe/c09pwoGUADPpSQjRdU1uBWINkPMFXQBccZbSSt+CrqblgLcDMlVdMMAF9AWxDXzyZ3MvM3pVWgrHRsjkdS1llQxz2ZPjljT+/6qqfzFfDjnR2y6ZN55x7wdlnn33WWRu39MqXjOS68a7wznvvu+GXN9+7b891u/fcMDzk/yxgCQlqPTN+zz79jLNO23nfnoPVdGeq0A+oH84BO0g/2kx/d0ejWt64caA7L33nqHEeLtoh/hgF4K2MDcRx6aYwo5aTENMI4bCi1k9piTgaIulv2dBXuHvqjl/cEJZLD774Uo6jw+qcSGeCxTQQ/LBNlwypNTXdadKIPCYEdYFi4fTk2O2/vDmf8R74wEt4Z0PjliadjO3bt9JdOHDgAK/YZpfNuY7DWSp65bKIPi2h+VgoKQagGD0sZBIJJY0+OScz5vjSODdSgi+EOsE7ZKVBEZT09vSE4cTUFCcTdEjq0pXByZVCyx8krCw+kns+lbvv3dOoFi+98NygOF5vNIZHR6hTfuoWlRUfjlDkaqunqAXZDFOTR2/jL4touG7Z1FGrlqcmxiQd+T5IRNKjmJxJgOvp0Th4HZcD6ZcLT1r86ECD7exZKcyMirxc6JX2o2zq4v4cn10oRxuNjk2UysWtWzZDmxwP6sdH/CptCx8wRZK+bDLRSeZWII6on2TyR5yFslisSmjEaQwn46MT06y3np2YPbBr+OCe5/zaUx77kLPSHR2T1TDHaZGs524aXRKOC2vID3BdWq1WBrsufLuasdoBZWKBxFZWc0NqiYQ0T32pl+U+/oW37m5NONAyAGboxc5pADhRHxHWQDLYQLMwFRYYxmSNDxKQRstnhERI9F0Lg7psN7w1HZrGQFL2za0IAxkf4vNG3vEp9xaCC849E1HVMzDw8MsuY596JpXQDgnAW8CyWPMedLb/4HPOfP6Tz5zyvPuw4t5228233rLvwN5aozY9PnLD0ME7fvaz2bEJzG9sxXs47pMam3XIpsw0cnYjalT7u/L1opfSo2mOq1xMExEsZGacEil6vEzIashJiIgYa6z6ypIl69M2b8j7d9VmZgd7ex988XZKV6OPDymKaAQ4FmewSkiR/rL5g+y91dnps6PyrF8bG957en/h7B3dSBkq0SQFIodMtm7azNDayDB2UWbriiVTcj52p8KI4KqvclWpyTN063gpNruFxPA8XHkYB/RZSisTW/hQrYoXIp7wXVN2mH98f5xRmJTfIfUgFJIpWRlISNGx4eON10zFu2/v/pyfvuBMb2JkQM6VGhk/AhUytOn7UK4Jy5Yu8BMnplh1pM7ZWmyyxpaxHIEr1mmFHP6iWHMqH6ukOArppB35SJZSOL1Ts9aUbId6WNafdJ6LEhD+xZ9QVHi85LuUUSNmuU8yLwH8JQyD4ryZLxXxN25o/0eTkzjiEEekgkTCAwAm5AzrlnAYtGVH7LhU0ZBzQJeTzjci7qLTt56+ceDr37py8nNfvu2eB73wOY/rzkhlYxru1GEcCIjoM/RVLXfBT/JY5CKKkEdRz0CimzwU+pUSfEwnQXjiY/76Ui8y2L2A4wv+7m6tOHA4CFj1/E00kI0NDL/lLW+5/vrrv/71ryOVHvOYx3zwgx/cvHmzwS2fL/t7fPKTn2QhE8cd/tmf/dk555xDM2YfXQK86lWv+u///m8mMb7sZS9705velNi0V6QAiRkTUKdp0UIe9sALLjz33K4OadF83PzhmwbemGnGwGGdXoLKL94OhN7WAe+yR16QedwFWFb/f/beA76SrLrzr5ejstRqdZgOk7onwuRgksnG5GSTwcaGBRsWr8F8/izGOLH2YsAB1oZ1WrAxOduEIQ4wM8DkHDt3q5XTy+H/PedU1XtSS2qpW9JTd9ft16Vbt24499St87vn3DQyzp7vMw899BBkjwyN8p1cuXMA/CETE1HeB05SCcCaJWU4zuaNvddd+fizz9vVnpLiiL8sJ/M0kXWAKOpuBFkgt5EoHZdGNla0wCRSp+5s7M7SW++IJs/qaz9nkxQqs5RlHwXX4XNvmoj2Hs796wMwHvYNzqbiTCiKl/M7Nm7EWCcCIBRGSwAYOKAexay9zelsSx8cHGR9avuJAIAIFJR2o1BHcxskURdbOSWEI/3mCqSmAAWI8XEGKUM0Knv7S6huoyzz6dChCmUSa3FSZX3GgbJkDgBHnU0SQIny5vWh9JegUAzF3JPk0FB9eHzqrLPOGuDMniJ4GZqcYfKWOapMylkuN8UEwRByXymnAyTZ8l9H76UcxsoZAmlLJ2cKhalpp05bJI7WkPMqkdcI7lTMe9Gz8j7BG6uITgHhxA0QOKu9jRPMbanJtEZWtCYRPpiDP3CZw4kZ3elrl6on2S5VVvpi8ocb0j0NaV9I8iCdduAAYL4UNrgnEBimtz85PSXx9TVp7vouSI7hH4iPhJlITxGXXnzRU68/v3fbzk9/9rNf++5P733osXf8zmu700425hydrvVlJamXXN6PGiXmaaJa1NyGSktgPRuEsXU8hAnB2tzwGAAjKgnhVojjDwIGCj27iMQOXCs4oG+8FQWjhw0NDaHmohhx3uoDDzzAgcH333//bbfdxqjMVVddRQQeEeHd7373T3/60+9+97sA7c9//vP3v//9oC9xQOgnPOEJpH300Uc/8YlPcPoh24BQFayXpF2pOuXyzF6U8aBSkQEgmcKQTbIER0Z6uMZrlXglHytPRoqjHNIXKU+ESpVoRWYJd2LiizmbYk5f3emoODuzzuO3ZF7yxMe99RXP/P03//pvvPL5l13QAx74L0CEqQ0TCum1uG7Dy9OB7tSvveh5z37KZYhLCFiusx6WdCB0xMeOq0FweBsM+CuQ5OulOCR5VzpRmxppj5Qu2bG5i9nAwkstWTUkfB4ZSChS+DWYRRolNt+TueXSwZHspemOuHPROdugzd6UZiyYQM5MHdnU282jPQdkMbCHMc2ZLepX+owsZuSqjo8c1Z9mZtN0hbimmliOXlk8BMN5OSxZHuE05t7uHquyZbto8Qs9lAKbHcManZ3t0DU5Pu6XC5M8P0wXxisOSzoWIDGTdfeOrfRJ2tLYFWOslsnPxx/RavSEH9pLZ1s7xhu5R/DaX72xCzXs7+lECg/BaeEU63FkytrEtFOph7KZtMDRSTtjs48kfLw0Bva+OOmMl5iBcJ7/8jpxje1BRP0dyjmcVNGWSrLemQhpHfOeKShtMETegL15Ny03bFrJDcs78LN9By3M6qIN3H99Gp/pD0zeRMkeHs7nZxhL4riRZz152+/893dkO7vHZ0pveft77310ciTP2XHSvyem94q8VqDZzHtxY0iLES8vWVVc6RNYfMiz3HwNuJGpNRGJsFZmiHnrEAQuJD3XgDN86mi0KIIYSNkX9x/+4R/e9KY37dixA1j9zGc+w7ZZt956K7ovHTdg9V3vehdYe/HFF3/jG9/4/Oc/f8MNN/AZf/GLX+Swhy984Qvk8/znP/9973vf3/3d34G+AwMDZL4iVaARZ1MymZBZWBl2ptfPmP0XWdpMM48xqTZURWSF+RixNHENM1W7zLhPPZQvVWYcrMa1klNhmk3Jyc2QjFvAhm8k2yG6MuLV++Sa6NXZH7VqmclJtbIMvg70sZhTJ3fYys+muMf1GiOAQz5RyipyZLdOZKNS4lzpbDdyZYOfjnSsPVZri5Sve9x5TCNBbVW5wh/kmKRz/+BTvUqSHeMohTAXd9UKbf6Bvu56YaovE77qog3kY1tPGABzJYTf1o296Xj8ob375+fPMWXNCnArJvLU6i4ky4JltGwYIHYFN5xk8shLrR5iSCR5BDg6Q0PDVGRDby9QhKCDtuU7v4BGUoKYgGpGICzAPED+ikbCa7LS0YD1KAgJV3fX/Q+HI4kLz9sOJe1JkbPMTWCcV+NrT6jRe5MEzKjg2tXVEfO25gdNhBSZWyYVpEnjNm/soad05OgwBcnxj/omJmiqTliOi2hij8ReipMmIeWok7J8Z6GGEHzaftX8CCvvEZ1e+q1NNAkV9h5pXWLFjyQ625hfITMJM7o4vsRcfBz2CP06Gp0ibRulMj1ymRNKJjIG7IRnvJ07rBRUS60IEzejvGaYirZAfxf5Yb32C88J/9WfvXFjf188kfrLD35kcITFTfIeG44MeJu8GF6HZdZ45voanFUA5mJjwGwbABlWQX0imyDx6dHTbSRxeUCnyCIek3sQsFYcaNkLYP6U1RGIRc3FyseVzhotGyMtXymYQfv+2c9+RuALXvACGjGBW7ZsAaFRlHl6yy237Nq1C/TlEVk961nPIiEKsX02K8VAmYFULjIVS6S27mCAJyk71HGyKosNKvrp0JQJIAbWVIaI5EASZo8xh7nOVGFsQmw71JaQrzHKsmCRtkCx5DeHSv9jUwzmA0QPZt0EJSIdSrkSKslyX5hlCYrwNepXKoturVMshds3qmQIQGKdFvke3dAe74yUL9sRRjAhNRiHV6yFCleWabbHp8VAV7N3xdmmvp5oNd+bdM7rlko1O5bBkC0E9HVmUonIITFCH8Oi5gTH+j0G8rfhVWDTcUDyluDGU4ukVxOCXNVD1eS8WEZSqYLMUfAqfmyZi4RYnoA/HnP4PK7VRH+qV+xDsAgsqnNjynsRqKSF8KPB7DlwmOOUzxrow+6CEpxkQxtQloXACzgOA+YJ0xKps9YJPJFRBE75NabiZ4S5py3DAjo2nnQBWKMySZ9OZSoRP2ELpVsLLZqChOE2qZiWrGsWzCKqTPX4IZFXw0n+MEH50MjfjroamymFonHOnKCREyEZZ2Evqw8q8mHSq5G3QIC2D/1WuQfJCMI+RajsxhGOlHSPC4unldGoCvzE51cqVTKptEzk0wp36iK0d/3OCy7adV5Pd+ftt9/uv0WXFzoWJBQtOEvEq4h+s9zwV+VePWJjDFqQfTxmi2KtgaWBHv+rb/48vRyDv2vKgdVu/QtWhqFfEJfHdNAAXT5IU1tpLticL7zwQqzQPAVTGY5i3AhRQhyQg1nHDz/8MLOgcQz9Modr48aNzL3i9EPU4scee4wcSEJztAx5xDAbIzd4gOcFCZr/gSyriHFGvXRrK6yVgV/GMgSprG5lPWY4ob+kE80wsyoe5sgE1GMWUyaKoVQumpoMxydCUf2F2RuQfgffW5SVhXzwXm7HFu7pLTVGj0x8tLNNgMqyYyMvEsJnRz8GhszMyKJkxD3KByYH98UjDb1FXDGOPcQ5tWwq9sE/ffdf/ek7kEoQSUzOLta/yBxJ5/6RUlU2LVw870Ly1OXavA7o72pPDnRkrr/kfNBXftEYFjxykR97JDGHznF27dxRzucf3XOIQFdsLFzEnCcADKhG177KwLJshxBlKnU1FKuGokMyMVhCpjmPiQi1smkzc3LgFiFVrpSxaNByoGrzwKZ8oQr3oO04FT42r/lChIn1+pbNndSXr6AgQtFYgO1Xq1wpYVKkLBHfnEl1tDI2NU0n9exNDMtKhK1bNk3nC0NjE0Iq0peGiYar+Zipg6+Dl761P1nWPVh4qns36aELiugUxEkLPe1pvqkDh44AwMwSIGfyGJ+aYSlOX0+XDCcu25FG6+cltHvYyA5chPVt6AG0Bo8OiQ13LZyYByCItqdOaMOFdXz98OgkI+vnbN9qX1ZXW5YGOzo6RgQ6KtEEBiDeiM5bJ52uaqdPFo6EWLYEo5jFhh0iXyyzdxbCAM77X2i1RJAzpXtuYuTI56b7umVKQ5sjg1O8RDy7dm6ZGhumi03e/Br8EDETlU3XcY1QufMdBQmmSidd9qRjkgaSkM4NvVgekZ63qZvgOcx8pNNAS4P79n4Fd6X/wOoMIgaulRzQd9wKAvggKRZMpdEgKZAC+Al5+ctfvnfv3k9/+tO0EhzACX7wFIGi0zdkSiGSCD+tCj/aCVKSkKNHj4IutEIyIb7V6dChQ4wNM6ULICfECrVHy7mqFUrolUFKX4dxPdaI5alqOtqbpRvKp+HqGtrurTi+DX4wXa6KLRiB9K895yoWMxlzxnGVzN3nKv/9aEv1kB7ggxJOasaLuAcUzQy4UBaQx0aJxMYDHyFV1Uf5w3+o449WVzOgAo0bDVn4QsRnPOnxqXR293nnZi1npxaLuaNWIpGjMcrtzjL1uz7FTk8A9sK5zfuEYUwhp8bu81UW2/A2qtHY8EwpnY639fRym5/M02byuVwqk4QVmgkcNo/La9k2KRqfsXN7qlU6dqq9yLtfemU1Z8mWRCop3aSoSqrhiPmPzYdp5NIRNR7oI0kofRUhDspI/vC+Q5C6fesAITzgpXRkkvT/JnUJrxZk0SVn3vO09Gwxk876uk1F01wlTyQwWfW2pyqlwkyOI/i0rWmJmGBZNkp3k4KkAstwEp3vosFQTStvRKbFKbB4GjA0GA5olNW5eCZW4aaVoDZd6OFc6lAkOjZTqIYinRzSpVyln83r4AxHaBP5pHQ3KNPbunxHaMAudoWwK1eqMv5Kb9pNIbWX3jnxo2LSkI/OqaVZ90dAFcVABsR5U8kofWu2+cGmLXGh0C2QuVu6XhwaZIXAAq4hzTSZWf68Jq0Iq/zle7CYlhNxNXjhfBcoLgheDQ4s8/taORLoSKL4gr7gKHiJbor/LW95y5133vkv//Ivu3fvJpB2w9ok0OLAgQM2WZq+JMcMY3YGj1F/AWAQGnkKXWaIvvrqqw2DCSdw06ZN73znO++++26eEpkx4+XXgJbKFzXXEcSHyI9Pz34IPWnU/CeUSS3M9a04adYsMa2p7v7wZ9jIkbX9pNH0iMVj3oFKKj5w9xv3i6bMeSjxHy/kgZMq5WXPfVExm/b4nS+J9KAxmTPRG1BAZIhtXShhqQTSQIg1YTFf2uOEkZg8n3DFuWd1iG5dyE2TwIxmPEJSRLDdiwma0bjE1ExpcHS5FcZoKousUIM5c0vfg3PfgYl/+cLXP/OfP8yrNhlnARbKX0r39YT7TSXI61MHl4VXZbQK0ZWzHFABWVgtVYfzYi3pr3KMxE3OG9eDjdksGydUME7gKBGxKx58SGaVmihVtJTb7noQSMRiCWHkxUthWXg4Gjs8MiYNQkwkdM/UfK11mJiQxk8nVR5IiFrByVzyZ/qXJCIffgMbevlSRscnJIh8lYyZYpm9G5mRBACftHOno0vOUjH2T05QMwpVSk86+8UzkPpKB5GaKmv1Vt+4GMmYsHl0tBaKbezro+pUNhWNCgDLQcE2CU9yh9pmUkulIvtYIrsIJM94MsFyphkWcnkslTT42WmWJq0nh/HR0bbbUmL0SshuWvlkvUqJbIaFZb7A+UpznA5nqXiY82DWrdeOpGDKMgUGO5ZV1u1byvclLKBeQqI6bVnWE/G44j0K/q4xB3hZrXHMQKHF4NDP6LthFH3rW9/6ta99jUlVj3vc41CCrUN3/fXXA7r33Xef9ez4bpldsm3bNoTLJZdcwggxT8mBPiZp+Sq2b98OHhu6W8UINI0ZIDcRsMwK00aVSyYdG1+yZEObpunzM4/YqoiLwskGfCAwWwlyoB4nlKHcI9O44pcVJuCz/ubfsYCMycUK1esyKZ4THQ7APVhNOB0XPj+MCnPi+LfUBYe5FWGBSFKZTA35XPHKnVF2Yh8uqYAw5vYAw7XcdEwlG2JZJbPLWWattDF1vKszX64dGhbeLt0RuVqTalrj4ZZe0X17B79+48+++v2b7j6Qn2YXzahMiaOwiuw1abOjwSdXOpHEKzE8M0NzK6aTySyDDIQzqDG3S7R00nwAsAKkvZBnV0cHHVDaMBkZE4RFQp0wOMQuYVqF+x87UKuUdp+9Q+jlNTB229nGllZHhsaJJmudxclEd/kTZsGbZOguArZhZVtPo9WjRImn+Wzu6wIDsCHBC8qkaH7TM/laKMyiWMoSOpbphP7ZTulj70YxIGFO4CFrbbm6dMyOvMJ3WgGpmXx3OKGF/3wR0Hnw6ChjE329nbREOisZDPy62a1UQRNqXfQr1WbBhX48Dxm/t3xSqTRKsPX+PV5p1JAcnQ34zdQkSSwebWPlvTzBvFBiBhyRM+0d7FA2MSWs4ImWJWNe3B7XEd/eurQbrOyoAYxZ1JnqxYcrt+RvLQq9WypL55FQfSSdC/eTO245QYTV5YD7Ele3kPlyp7kgCNB6eUj7AH0/+clP/v3f/z3ICnwy00q7orJF3G/91m+96lWv+tGPfvT1r3+ddcBPf/rTn/Oc59DiX/ziF59//vmvfOUr77jjDp6ykph1wFihSYjo8aUMt4Y9FITSPB8ti4SZGkpjBoHks1WxKfG5IZSPVrUP8fNDoBQj5VK0VI+WGHzE4uuEkGx8sd6PLdn5ReVXjRYqkSI7xFlarnwgs3/6maq40AKVgEWIne8RXzMasH2BFAEAE2shEzSl29evJLHPu3UXoEqkuC8ueSoRlyYp5hDF5FHF9XKKOWp60qJu9a0CQStfZVSC2bmbBjCCHj46Mif5cW9VhZY5nxCNBETojpVDlVTXYK72te/9FJ0O+yqzUyvFMuuLRBp6jor7ks/4MD4uM7DY4MU+ElGEDMy8JEv525ztnPhkS+aQCl7CUtHctRUJ+NLdYeCcbX5DzmDBOTqRT8TDm/oNl+VEhb4OADg0ODzmvTJpKtbtAID5giiLb4drQ0/ihtghdmVRFZgmFap1ZKPZdHI6V+AUER4ilrmOT+ewoDLrwio+h+zFb81G0ohDdh5fYSY3fOB0AWmHVlwj5ur4pEictFW89kFJQALjPwuExqfCkWhHe5IXy5LxtJ2GVJKjC3BmKYAJbiYS5qCw8n7owlpgIpVi/+cpZhXAWuG114h0LIAbXgXvJZ1I6p5udPUELRksxrCRau9hp+nRaXcrUM3eLg2zQVPgLG+DJKWSC0oIMdja0o9X0+F/mbtgAFxtfMKy5l8IlmvgWsiBE/jEVoZaszDzTVpv/WMf+xgw+dznPpeJzQgOLIQf//jHiUNhH/7whxkYZpXw61//+ksvvfSDH/wggTxiWJf1SIgwNOZXv/rVb3jDG9773vei7VmrAtpNHvkKH/mjWC+fej5apCGg0EBfmi0/5DfsMwzmMT8C6Y5iYAZ+5bwgxoFQuGTNu8yW9DySGjBTZeY45PCZNb6048Sd/zHJjdVL1IARGSpCwFcwTMuX3QSMYHkE9SdME+xioBeOcWyFgp/gHy8IzHAxoc5xCSIbtm3dEoommPq7rOoj/ios9GL4M4qOLfxGJj18cLgcy/L74c/uvPmOSRnYZAvgOHIJnYD3oNUltuesRB6xCBjarNNWYUoO2Sk/vIgn9LdRHykX2wwAbHip8CR5ShRXWxEAvu9hTqCNbB0YaGdPFXkuS9662jMwcmwqRwVwgm28Ji9zW4PU0dnGIwGF5mqCkGqdlIT1Gq+DCvKlDA7pG4ApdSZhTXMAZmd7Vt71CTmPED+xVNYAOJ2W49HY7AICCDkmpp9kBTxkTsFahHm5oU6ECT2MlOcLxXgylU0zxAApZaZDw5wy48PaMfM78S4pmpFpwEkmsOltKpUJRyOmAfOdaAdGo+PTKOMTsr99Ji2rGeVFsPtHnWNMZH/vZHs741Rj02K/9uiUSLwXIvNbRDpr4cQlsni5oNLgQQDaI0kr8+ilv0ulqB6dUqm2OmN8gL8eP1r2F9RomaMp01yAW7H0FYtAJmBsA7q0GACDCPQ0Udf++q//+i/+4i8AErNXI7DQcRkJJhqLhtF6kSA2TZoMqQ9fjop1twEzBkM+5I+Belm1taZMq7WM3PZKKBZLfSYfFRhsf/AIVkU5oYFAniu8KERrNPuiJK7auywH+c7E/tn41vyPRBPJpfFMP0s/fCke0lo/hg4yJMErmGYD6vMmhxypGf/lD2uHZQANUWJUCXbipPrHkmnPjnclWyaYFmeEa4K24VhCpqOJ+IfWai3KlHjHOWvLFlbd7DlwwHEuOl6Os567kgXgIrOQM11x7rj/IQ52uPaa6+/+yY2f+8p/Xn7Oy1MJ0UNKZfauRyNpaAyzMpLtlEfJzTRgkcUyqVSYslxnvOQqNJnzbmi0NF0zSxgWwQaKYbtuprNiVIYld9zzQD2avGDX+dKylQIo7ulgDDjC5KmZqtPF3iKYx6WHFxLJ7S0CNmOv3MNcnpIWyc6Lk/Woum9DFF24vmHDhkcGp1kKfOm2DRpHTNCCGaiqs9seT0/AkYlxzV6NbRTB934CWZ14EqGAd+1+SfYihkbQdMPJdDoVZ8CFs1SrelwxY8AVGR+WWc3a+u3Fea9M50wxBqzAx/p/TBOhKJ8VBdCo1IohhfHJYGEhG4QVHyDvQsumNx9nxyqgkq5hNMvxCbFJNjsQ4podOXCmgktt84O5/iYAth42ZQmlSq0xnCQKwJwjqoislec5v8C1nANLeMerRiPSh14bc6bMIoq8A4wNeikTvAQ1ab6EAMwgK0IBOUg7A30JsQYHSNO4LSuSkJAkxCQVkfmEaIXkAx7zyOZnLatC9mHM/jz8DAjWH0LNfjR95Bu7HfKx67dIN5+vi4+NKz/t9WvbJx038+Vrr8S/nswbIi0c4YgXOt4zVTndlms8XG6P2JEpiAPV71XOwiATTHK1G6SIaksEkJX93MpLJA2wNG7ocf5IrjVG/ZkAmpUVXJF4TXYVEz6wB75ySMQCyzA7O9PMXxkZGoJJwjRXXngM9xjnhVu5PGUSNLOrgPYS4gaGo2dNTkz3dXS/8dcev33Lljv3H/z6zw8wDWZ0dDLO2W2YKHAy45Sek9QHmIJI/OVQbDxfofHITBl5RBg5QuSyndTaT+T6hHWUxTaE0Wp5psyZGFIlQplzxVW6I/JqZA7s/r2PMld25/az5ClzzLTvx0BqXL4GVDhJCP94SAKuZDWeK2CISdFflQYgvRz5STVZkSKDKTA9XAICI/kKe392stRtZmqCtDi4XSxVsNwk4rKlw7Ic1JqjXH5aJ65kw4/jr6XVpSAoVDUlk/22LP4qXckdDgoaSe8EO5Z9grxrqRkNIxyqMJ0cawkfK2vgiR9leVJVlrAxMlSVYWNmS9XoqAiXtYGWRK2sMV+aXKgOM+lpqEWmRrudVFoUwbLKh4FegHamyDrGBEOzWlV5r0SleOKxxU2oVuc0Zl6ZyzqZGEBEV71uYgvyixRCNk5iWAIDYLVblOty9HiEPai1ysQx7uKRctQMIo80Bxq2ZKL+4NJCDrhvtCUUgJH010zlBTuZh2VkmBZrfptIYsBMCMhqTwkxD+BqMU3Pw29JLJAigF6/J2gwbI+WcqWBwiD7NRqr+DRMROTsn/8E5NKTgPloUDf4wrnapyk9ActRvlOjwv3DE/txb7H8EN+zFLL9OFIOE7BTzLmOTNRTE0jzyXq6Or05MSMmWlwoUVcVkPxFXNZl/FWpoV4cYovMEsFNCMTy02jElIy9n6RcsqupUEcAJp1IeyWSqkRl70OOm4jRbWHjMIqH5LCTbXc4f6IwPnpwVKTYVAnckaMMa7IPUVl0aO3NE8hPZZF5OS0OTscZBAhHmP7iPLJ/OB2JbEtlznGcX3v6E/I9/f/wo5vuH3dSPe2uiVCMhnJmLbKWoWFmaEECCSdJO1xkz+zNvV1UNSYbR9G3ghs8XIaDaRCkm2KKTMc+zk8Xlop+dF5PticeeuTg2DjVRtIzeTsmfGbODpwvhxKDU9UDD93dHipcdmEbFDJ/jb4dGAmgDPRlok7uyMg4xHPaLtUAFViQOsMZxpNTnBx/9pZ+gFw4yoxjPfmnGglVEwxeciJuvjNarDLKHHfaOrvY/uzA3seICScZpD8yNs78s00bORzFNOpl1BfacEwTE1UbptJitCdapDHFE9FypTfOZq5hztAdYac4Pb1gGbkvNyo0MAuyzqZ04Sl5D0yN5AjOMBgKco2PjoeKI2dvyVrvx4mmID4dSycicbaAoZ0xw7la5mzgfIyZfWFnpixImauhHxc74+zGIzluaW+r5mYmS9WCvll4CijX6UJWigbAh0am6tXIpu5++XZoRE49Hq7TSexiMXeMFyrnnBxirog0P93oTrbv4SdRxfFX4JiXk3A4e1vbOr0KTDxicqbRJOLlSgmjxmSxXhZdI8kjfrWSrO3kHZBzRyobisXHp2X370oB6K3JaVglWqL78WhJwaUFHPBecwuKPjWKpI3aby650njh3uyfH1unvnBnuOVfJZE5fJa0EeQ98nBuvieNOEvxUQJDT2zAVanW8vVoXjXgWLXQlZDvm2+ZH58oV27VqiwBXrlGnzwhxH5NhQown5CT6VFMsEKWuYKPXBCUKAY6VElvCSIAlJ72DEsnh4YFYDg+mUAZVqBQ8QmdVjq+JocGYA+QmGLkHBk9zOTzbX192ZrzxMf3bj377H2Tua/98GaM35NTImOtFkhezY7M2PJMdBwKHWdko1Lq7UBq0fPTgqUbsAxHGtIKnwSNRK0FRMlcaZZpuF3JSKhczBUlXwJDYdllCfWQ+led2Hg5dHBksj2dGOjkvByRwUKJKLISuY1FM055YhIruxJvmYoUdyYKgvId2TjbpkqHgTERJvupSYDI4upsbSZrqrjt6etl18vpyXEyNwrZXIJ1wMzz5awki770q1VEK2leUdjJRWwcEM+oMzN1GXENyyovl5il577smNKnBGxpaWrypkAtk40b6wzHFDgrt1sPPWZ4FPJwcawD9RC7chKPEIz6rqmKbkRIMiE7GgO8omvL2EmajS9YSsQcKO8t0L8RtoUE3uBwsVQNVUMJd7G7NC2mEggTatIvT8dkOQSnqOEAR/mD87iuXRcvUB5gyQCNhVIyV6OevmAxcbEfS4Q1zREdXxB2ywmUkpMIHz1hmkYG2fTsJCccVLqVtvvg2gIO8EYCdzpzgK/NrPFY7PkaGXvDwobFfj3UmcbnCQMVJ7I6QnbtgTYMdgMbNrAT52P79ooQQYpIRdi3S33EnN/VEGpMxELkMNmZKAf2HCHDHWdvB5h59PJnXRPNT970i5vvPTASyWbrqBQioNx+kohQRUpCKJSpTNVScaAfFBPsF4mlsm/+kpcdKoMjGHIgj5ER+w5lpzVkYgWNVZTyXD10/6MHIvEYWzVBPKYeaFWxK4V1dfVwBNfg8AikikMsq90FJCBDMu/potImg6WOjMZwdRlOKTq4wO3Wzb1sLjE8Og7fyQMwKJdRE6sZypOAFXbQz5ATbTKvh/6tcO4LZAcflMPGZkExZrmNjU1QQTb8ohHI4mi145tFzc4sITPolCw9pmNggLHY3lw28lL0BEM+K5gLs4guj4ivwwf4mYBCEk62kHyMCvVxoafJQDtP/eK8J0v66xElRFK6DclhSlQCrMVKM+bWrIMWwRtTghbVEpZUVBBptTjgtsjVyj7Idx1wQL5PFTAICANgPvt1QNcxJCBRIFQEg4An5zACR+y7ghAhCEVAZkuLEkBAw4m48Z2OfiFLmTFVcuIgyr69h5Gs27b3kxLr9bVbnOf80jWHDx/88re/jRKMQqNqjAgpzQcOkbkMj4kGPDkNcPV1ebkzbKZTmbz7E/k753vr6JDZCcyhtYJVKSmynT6UQxu9iTsffIxZDpdcuItRW37YAcgBEonQ19OLDjPkA7B2XHiUqzi5YgHWsfMhFZFgOi7CQlnc4vKO8pSb1GFzPyp/ZXJqGoMAjmOKYCATLqT/snLOLVdBgzmAaGr0EpTnK1fGwjlRkJYFA9w3QBducGS0Vq706TIzuj4GwDZzDdS0zNwJ+moQpvVx7AItFJAmF0wR5MnEaWKyjguO4lx4EwAW3ZlaU016M10dbS4HoMRHTh0vo33qmixNv5yLD6UGwLLfFqZpHfiXbLQUCoVIA2CbJm19WY1AqwhcizngNscWUxEUv5oc4PPjE0XTQiLQC+aDT7HVZMucWzR/TCx6pKA7ILZkzB7SQD42aapXSoN6UA8gIvLLFN/F1FCZLINkLDrsuZ0YKzvDR6fYb62vV8qi0qghb3zpFZ1dbTffdfcPfnGEHQIBWslYSjT1l5JEls6U2D+h2MYGlq7Nj3AmtjII59GrCU/sIjWkxEiIuVS8nVyhlNORXaklA+FhmfwzrZ2AB/cP0me64OwBRm7j0IVp2LNL9vX18CpZySrk4rRfApcEQfW0ADRYaJVgFcNgHrGsaPgsKYBnMaLK0QKlSnWSs7tIPl0JsaYgI7twqDGePyvg1A4t+ZAt/T8aJJMlXWJWIPuFs6AMbVL62riIiooxGQ4Pj4xWy5Wejk74IjPDlBp2HeWF56ZnyBH+yCYnKMuSiTRPJjszUkIHwigH8LJp/DWmowub3dUNeuS0Wn3hp1SzWu7qaFcSLZ1LLfExgfASbRXTwnWY/4kRTKnUj4LAVzoHTRNopCzCcRZYLOt5V/RgpWpc+DOLHgkJ3NpyQJvl2hYZlLbGHODzQ/gix2WYipUVHEeoU4/XmIym4kR02K/R/lBARNzhBBx4urU/EwvVsANPMelKpaFKEwQhlmoVH6rSWbZ2b34qyyxekHX/YHmmUu/paO/Kim6ZZW5W0eEQxBe/4FdGxqa+9r2bxjG3Ss6iSwgAE0kyknKGxmSYsK9bpLOo5BrNxLfcnKizWkvqOguHxLSOykXfiO0atBDCgYYa6i/66P4ZZ2gyz26sAx0yZCirV2SVs6bmKN++PmIOj0/QOxEH8Ur6xFSBV9yZZRq55OlqPIy6a0pqKtVjRxJVphmHpoLt7VlGIo+OSNdjYpq5wfXu9jZiqcIv0VfMKWj4GvCKZbtgRlRXEFf/EIm68hMOwOXRySmWEPV0iP0W3sg0OzRatbz7GjAhFbEhAMpyyTExvhZiTjPxld9yljD2pZm8qwETv86acXHSAnGmAXeziNtz2h9wmzAzRvkkbR2a93xJf/kC5H3yRz4coc424nDN44TJkjQddUAD1g8ehPbeJeo+KeYak5ZUcBBpRTmgjW5FcwwyW1cc4Dvzhtw4eUcAGHxqqe1JcUB51Gh8YK4IOJGTJlkQk5v6ZLPcyekcQ3WmAaOKiNTwTW/zMhrzsxy9yCQm56FDI7VoamNvdzviVazTlS624i06z772st27dj2w78hXvsf8U8lcaZLZYb47NMgIbIzTi3kEdME3weEG7X7EZXisvlJJEuk6EAhNZbJA4fik4qIQLggBSSDErfflSk744gsuEChl+m4N3VYnUGuZ3bJ6KDQ2LicJipMp65KK3TnQkznnjoyEmdBMv0LMH2aPNqmM7i+LjDlgi2gceMyOjOzLCGaMT+WBoq5ONH9qbCRL0pN0vgZMPn6DPMk8l5Rca0Adha0eEMOLiZIzkS9mk8m+rO164taUrarZKaxQzHmZK1bBC9WA6cWiZSa8MWCCObQRQOVAJPIUbnOVyW105uTgUQJl5X2t3EYXUHOED9aMiMzPGwM2879X5tL/Wi7yht0x4Lj1I+hKaV/HCmVgGLJlFoi2cBo6ExCUVnu+9PKCmCvMgRX7wFaYriC7FeIAXxhGTmQEHWT2quM7NFPkCmV/4tnQ8qCtSQAQYGHyB8hhH6ZsKloslidzRQx8yDJU0iaIbE5rZJAOUYTcQ6+T+AfHp2uJRG9HNkUQ+r/sUFnvSYgh+rlPe+ZkvvKf3/8xkEMCz6lsVBP06GSuFg5zghzLpJDdAsFi/T1ZZ1WWWouYFu3F0GgmX5bM+Y/cVJ2Lxw8+soe1O+ede7YoOayE4QcJWksu7C1MtwXmEFMEK4pcXdY9zxTYX6LWwSRpy1CvTGwWfUgVaOEQBlKVwBiaqV1ne1utFh2eYKENw5wlJtBie9fuDolX3slEIW2QK5/1nBy9FsZfBWAu9MSEzeN5p1CuceYHjQEziDJVEnOgH1ebr4SHmDqNW56jUUoD0i9I7lWxZltTGFtmxwGJKQ64lWR6njR/0TsZUpBTJMWRTl4+zrCQb9Og0QKXfrXkfnxupY9IAViQ+IOCW9eVDkKJBHJV4o1IomiXVx4GrpUckBcTuNObA8g7ExyMYPG1y9aDLasw9kD9KQE0Pl8gVcUmFmITImSFiUvw45xtWxOp5H0PPYrJT8BBRIukqOi2t/NXIhop5nJMjCHeTXfePVkpX/O4i9mTW4dvGeys5krTWcd5+uU7L7jggtFc/ovffQA8KrO4s5AT2qBCtwwbGudMXGcD5+UyCFiqMBkHZFQRN3+xxw/V/QWbo0Eha0l5HRxePSErdxlfxjLMys40JI0UnYcevL89k+jtbJN52GyXBPpKH0qqhkGT1VGZVKpcCx1l7JY3ighmnDji7DlwKBpP9nR2IvMlIY/ipuSxyFjs0LrBaySUTDMCynNCztq6hc05Dw/LIUrFEgfSl9ijhJm+Nc4OWSHHIGuxWgQK4CoHeGN0ZXKZosMKFbBgNmwAIlAkuApvpbrCrQNDhXos1tstXAIdCeX90pVJpWXwOz89QxJcqVpKpjPSW6JfVJGt1LHxyqgBDUxfRFcbr6U6NiGvDyepErxEMVvT5WH0hCRtmXRHXEolBd0e6ShpbJo63S/2PziBU9pcM5Bu8ixlhmTSPn1rJoWJsYP3rDtTMqCNbh5nT5VwGF2cUQ8sQFSQHbDFCs10icC1lAPWzFpKQlD4KnOAzxLcpSduGjASZD28dRF5+rPas9JX1DKVYIgoJCUg2t/VBjAfHh5FpKB/0LfPsayyVmVnIU1F8DGOTFgNqTlPlliD6fS2sREaAGYys5plByxd0nPd1ZejKf70tntmkEec7ceOLmIdBP+S5Ds+k8cqm0mg4YjsFZmJ0Fym2mBC2a5GKJxX5suSTNFaROWSTcsZdMTPBqplPccDWu+5d3R0cH/MKV2yq0eSyJIiFZdqu6aC1KKzvR1WcRCA0MZaUM2QXZZRyJJxUeU0gZas26Oi9mrphNDNCbHMlzj8OtoyNx90CwAAqcBJREFUdHxGZsQQOjE1Q5VlaJNIasbU9CtwsbFPsqTKZOdrmSuQ9cJZ8EpxXFxWCAYL/I0y8zwc6cjISDlGDlbKaxOT+Wh4Ku44LkwSNthLI4dSBTuI0O/mZuBdl3X29A+x/kq44LtgOb8CO2yEQqmYaqWEqhMzC//0hwbM27fhW+/5Cf7lGyclQMtXZAAPKZaXbGsqdMkt5Uo8a4aQ69dEAgO31hyQFxO405sDZubiI0cD5iO0hRatq7KJM2RB88+sZAgw2RgLmUAkJOPObZs4NGvfoUFkGboY13IFrUwFiFeBWS1Y1Wt2UeThaI7FIflYPLKlLyMiB6wJxdj7ISa77BYxSj/56o5UMvbAngOPHjVBFS4CXCptKevIyCT6aFcHu01BCelFeJ2km02q4BEhzLHCzjwxNU0ZUsd4+mjBeehg6fOf/mQ2Un3u054A0IpjKVEoLtOiGMVXDRjC+nu62c3q6Kie3gMwMzuNWT9Mqq7U2rNyGLCUqOqWzh+XWhAiglrsDSKMuSUfJlTT2zgyMkElh0fHyAbbu0hmukIr58wQSq29tbZroQFTAX7UkRVc4qRWUvlDo+OVcKS3u4dHhCl60cdzOH2SSjPWq7HdCQfMM+YW6LUxYJvopEEO5yeBaMwhtwSSSkYKhHE016lpvKE2NtfiXbAyTDO1i2YpU/DoiNhW0k0Pl+G1bg0J+LSphXQO5K3rjELNBvBHAuC1Ho+rOlPrWb0CjRpc1pwD8q4Cd3pzgM+Pr5TPT7cEqK/PRcCcf4fMEj1YpCETfmVLyR1bN7FX/r79hwannAQ7NaJwZJIxTmtQ8xoxRQi6rtGSmY1EVoODM+Vioast09dOjhheTfHA6IY6W2bjy56Is3vH1lAi/dNb72E/XkR0NJ6RM6x0gezg2GQ4Hu/lRL96WTRzWezhFbXMv5pOTY9KsApuWfKEB6IxQfN2jg6PIq+xlE9yjTn/83/+YdKpPuHy85//5F3CERw7hvKTXZxFmSWQKzOtuDs0OCxSVekjE1RYEKa7s13CNKnIfW7ESaE4tiqRICChzM5fTMLKVGqhwZFJgrGm8hI62tskxQnXWQqZ62S+Fyo4lnNdhn4yqDM36wXupUVJJ0KVPr3hYqvNhsYm6uF4b2eHMJNhftEbhTkpOamjls9NW5ZhYbPAMAlhRqFQAuToQBgbiZNhan1YNGNbRi1A3qQBj01WgTlM0DCZJGTiO2Mts6AZHpp3GZK0uVkp/KSux3LzlnPLEC/1ZJTBfdX68oxsrwuOlu4t5aM7MYegudkH92vBAe9lrUVZQRkt4AAilc8P5YPv0xZXyAk8LSDkmCKZ62Q/eSIWUagyqaTSCmlY6+9JX3DBRYeOjvzsF48gLti8UAUfJ2UBNPM6sRFjZkXS7Dt4IF6rnd3fK8ezsRchai+scKKVYh6MY6Nlwp96zRWg2k9uuxelmulLZMpeG2XVFDilNZpId3OiH2lFqmIeXlQczktOc6DgQJOr1RgU4B4dCA+Hi/AUAsDh9/yv/3f22Wd3JUPveM1z2lVwM0FLrPLhhBNB4ZI9lsRQwGZY7W2haOzw4FGzTxNGXdmcEot6T1enfNsqpFHdpGPjcZuKiCEVnku9SjzpbAfHY2PTOQhyJ2Flk6InanIp6aQdOUkPSM4XAoCFNAZHVy77BemTOfE85L9yzOJxN8zCr3Cks73DoBGItdeb0S2rrHMA16GZIQ9rlrSrXIHGA0jrOmDNE3wWY38oMsVAAOXAUi2JP7ix8Qk+PT07Ut6+2h1midyODgFgirP4mqhxWQSApRxzShwth3woXXVdfSDhEok/qOx4aGbcmwYsCrI0A8siuLaMA7NaQ8uoCApeNQ4gdsQq5WrAIj5k5cOqFbfMjFU/aaIG65nJCOnHs4e941x7/XXhaPwnN91MRVBtbWqu2TC1rCaxqveCMmrLfXjvY6lwaNeWrXpfZLqRzMSCGSgociBNMZJzrr80zYF0BwZHRibE/IwUIzvGodmLv1iux9NtbVldc4z+44nxZVZwbnTve5PBbjPJpjj1Lxxl5hNks6LoY5+88aGDw5VK6QN/9HYs6ZFaRWQnxk/EpXosRzhDVt1dnYj+I8OjhhA8AkEnpmcY9e/q7NCyJBFZy9gvMptaqNAV/FUH7IiAZtC3o6tSDQ1Oq/WVrRPVtKr7N7sxT/KPvhb6AEIP24/gAOCTzPO4yfVtNsWCxYJpMkNqZHSczhbdFHoztmeVsSSdZhmSrB0imeEfwIYPB5PpwgJyALDcayB8ZSIVDJ+RFATKdC1YTdGUMj45xafHcIBlTsgc10YHT7aiLGjbk8VBvuPN6MvxA+Z63Mj67kluAAzUuo1B/5CfvF8B4LA/0oy5RzsK0iIC11oO6EfaWhKC0leTA3x+ngGKMWA5OBmVy8TBaha7YN4LffImKyEMcafksV8+oOPs2pXdtfuCvfsP3n7/CFbkkq7WURkzX04uUAoOPbrvsXQofN6WzaL5RUPkxWFE4FYimXaqzDYqZSO1jOPsvuAiDka88cc/CUViILSQEYqMjLIGKZpMZXT1iJinTXEQyb0CjkLEIbW5YuSmP4ERcjznfOu7t9985wORTOcrX/lKaKvnRtvCTFUuC0OouyQyEmvY45GpG3r7AFe2cdYHoq+x1Ht6JsdRwayfkqjKpIacBQBlEZJUR5+wK4toRPj7+wdC0ej+A04hz1LXWjopAL+CzsDM5gHZAWagzgrmf5ysBBDF+S9wcHS4xi4cPRmxUGuD0/MXHLZ25paxXg3TJJBO70fvbWzYFEp5pkzkljPa8nnJW4DNdEx9ODWTB4D9L67xIng3SpHtCYtuKuYgGcSVXH23dAAmNwNgnd9GBtatMAIbs94se0qBTsp3meKXF3jWnAMr+5WtOflBgcfjACOpTOyshOO5SpSVPNgeE3G2QXLFK19g45M3MX+8DFf0OWY/V8jQECmfHycQuUXolOjusHP5RecyDPuTX9wDrDJzV57athIstdRDC1Q8ksqVJ2A02sih0alEKDLA1hsoOGyWoIOmMs2pFptmn0nsj2yDVHGeeu3j2zOZb9x4cyXijq0iBwv5qVSoAEJLWTXW28qBqkJeFRBfnkMqS30oX1KKXV3pRAsPl2pSjUwKb3VwfOSnd+//9Je/ngxVfvsVL7rinI5iodiVRnMCMcsNUyEMUyxhsyoAtrM9iwY/k+P4JHHlKoORgEeZLbaYuiUFinInRcpbFukeliOyYYWcPQVdRBQJwP+NncwUCh0YyU1WZF4uZ9RjXFXFmT/LcFAl3QqczAWS1gcdwrqa2DPM0a3hgL9CTQ4p8hHRe7iSf/1GJaAoREhN+eHGJ/JsDsZ+X95UpDqT+6CQ6VLwvFAN0dhkaJioaq+nncI+DgNmGXA6xHQ4RWWdVxWNsqgnzjwsYTiOkQKd88atKJ2hcEKnVnOLtq0kSe+HloQ/LYEsQ+MkwVCJcwx5X0qn5cSVoQciQ5iSbU+Z5+/WRd8tO4fLph9QRmeC+dkSkwhyMJi7roDaM+OsTLfNnkhc2YZFzTp+UYGnBRzQ19qCcoMi14IDfLd8c5lEPFdLjFWTQ9OVZFZUI7FtSvlidDUJKHKauCI0V7dJIBnUiXBwf77E0LKZRSIReBqXqStZx3nqFeeG42033vnIMCfIIo1wWFShux7GpK6zT1lHwl90ZwZz5ZT3e4ed6SoTmEObe4mFulhJODWyEsHkJDLtm6oOuCcTu56y26lOjIyEUrcMylziBJpDuHLowAOZ3N6LN3ewwWAxkqiEEhU98C2BrDMChIjjO7gKxvBjb2qIw/ipRmIZymbaDstgkPLtSST8VKIz8+f/8ul8IvXmlzz1CVvjHUywSiZ0jDjWlkwrjumiXnjGUl9hnRyWsK0nXMrX8qUQa5hEp4k47JydSbb3ZDl1F+jjVbIrtLxsoVo0M5Zb8fIBlgorngR8wkjsCtr21myFUefb94wMhzrYKq0nw+pnbRTHr2UjBtAVEWSR03Mx8FadhExqozi2fALrweAUDHZ6Q9xWy/G2fdPaO2lksMI+3jZ8oJ9DkwCi2CMjHJLeySP7R+NJujcdMF+2SIdFYbp2NAenH5twJX9wqjoljUX6TUlZzyXIx24woxO5RDS1IZ7ABs2EcqbUU52ubj3SitVg1DTCaYY0rRRpSmXGjGemi/n+/j75qGAH7w2mcjwi/GFOWlXI29AN+8OHx51qOFasAtj5Sol2rS+Po4vxCSU19nGTFiQnVkozgiJZTVih2bNDqTOVl04psw+zNHqCeOOs9IXf0mNz0m3RUigxXZb+RClX5hBGGlGlXPXUZS0juLSCA9IwAne6ckAFUJ3PruTE8rV4vsKMEvZqkBk5fNLUGvEholmd+teyPVCW/VwCoNZ+eu8+SjtMpIpt3bJ5Mlf+0S2PMAzMiTSKJcQ91lEJmaB18Oh0KJYa6N9IJMQTNjjmYLEngdSWc2+cmGybbxZpx3niNZcVnMgPfn67KJroKJHkxNR4NlwEkEA6NAU0DNF/UT3RHlWnPLbgRUI0V8lYKXZRLaZASH7gYV9nenL0aLKt7eprrrn2op3n9MgJO/KORJGSJaecOKuvTAvhXjOScIaBOzqZfDM2RQw5rIFDjTgNvrutg0fuuwxJtdxSfY+ov/BJlCcqBvBs7EhWSzODk/mZepwxCxRBFLtFKrXwI7KVbhJ/rOJKrVhY5aeO4hJRFL5wjmN4F85oZZ7ItGuQTgGYWstyodDUdD4aSbRlstSQWXem3dJTg2nSMmgokbhgIK5uo+RiNqANsAw4XI+mZUGYUG6/WCwKwvMWPIZHyBA/iv5UbgaTAzOr7WVphsIX4ZG+IIpLcl63zH0TCut0VeQJAa7jXpywzu38EcKN2o5IQYPEmmH9mFAsoocg0gmV2NoJUp1b+j6cwF0X85EuEaBRy57wlndwbSEHTuwbayHBQdHL4wBdeG8jjqLs9lCr+TOYTpHvj92hnKdce2klP3Hjz+9g2JClwMIC3VkCRQSscmcIi9ADVMVk99ieR5G7Z+/YqsZbE2f6lIQmkpSLaBRA0FOvvzJcq9zys18gmyeraKvhwZFpjIrdHe1AhUzS0ciid4oSuQxHbHKwH8JdHFJaf4hoRpRZKgT9PfFoojRz1a5zX/mC63oEFFxns7S48T3eE/cvhPX19mCjPjo0LLnqGcYslGZ5kldbiUm0RqZ+FmKwtGCp38aNG1i1NTI0yFbGcWYVEUSGGLVXxzGPiaborrZdnSL8XA1pBG6wEtQZdggNj4xFI+He7i7hDJtJNSEePTzZTjlUO3aAmsg2j4lBX2GZOjjIEC/5ekuHld3SykS7ZXcqHsmpz+75y14y/WsISHLuOP+QJB6pbvaLQOQc+GSXLgrS6ZaziuCGTGy2gbcO2G0Lc3KYmyy4XxMOzPNhrkm5QSFrx4EEB8zWZRGCTMKqVO28lyaZs3aUnFhJgMEvX7VlS0/7/sHRmx4oJTIsQ3JRkSnN7PEHSpRkqExQAzWGW04RxsR21tZNtO+IwgzSrcm5dzwF/87d4rBf9Ojo+L4hMSpOsQvHGPpPmDVI5Oj9kIlsacjd8j4ZEhgAy36I6LEYB7mKpTbMOij6Fqgq11168Que+uQXPuW6TWo1RlDKzFvFBq4IVlwT8bO8bFSJhYN5WEzjIQ3Tbtl2iaOQfISYFVt7EFoBLvLXz3cDhxKEahMT4wSmmIKliphsALYKjkxZC0cdmVO8CtnPzdJDNQlHoWVk9+joGEO+GzgISdlrCSwaE+MwALCZhaxW8x64fzlZga2t2IPMZpFpKBVo5ziNGovsNYFWyEd0ABiA7+hgq20vtuVlV31JbcyExoY8leNKF4+rUbLwO5fEzdhMmYxW0EiYj23v3aa8WRxCqBFJkABcDYw1hwXaCM8Ct1YckPcduNObA+zDQQWBXgakUDuQfX59eWC/9dwOmJXVG3GedNWlY9Ol795yN3oqJzph5ZM+RG2aA3QBHkQLNjdUOkxyyKPxkdF6rTzQ0yUGXOJxDIHt3au3hOjQmNQdHRp2PH73eQDkt378c8bxyP/oVBlU7+3QE/042lDG+5iRJSOmkt2SHfmjQMtcLn6QJRiMCZBM0LFYgiubjbRHnJc+48rffPGTr96WYs9qQtBjfCkJSpniMm+ZvLXObIpB8/GJmaIaNjk8Kh6LtGUY+zvWEd1/zxDgji9aII0inWCHkyLjgqKeKr2RxqrSY3NbZshsPLENKGy1zzIzOpHo8F4GAADDcAzz7FHWIFUKG3s6CLNzoohgsIduy5n2+BuLpLxeF5Ft5rYLwNoJJKHVxd+7gxBxioHTMzOReIylxfBd3og9U1YYIhMGAIOdLATnYTMA02glyRIcCfUsYDkM+NgkFE2LIht3GZJOY4S6ZVpzlkBHEGX5HPA/yOUnDVKcIhxgEaDY2cTix0bstYydtX4qEI80EfHh1Bkqe9oTrg5FI/c9uvdh9NSIaHu6+zFwKSuTkKoKc1iUndG8rDFl6tmGHjFfA3oyRUlw0Fq7TUV26w9AogQ/4fLHpZOpb9948wTGQCyHORGPPR0CnwJTOnhZruoEmmXxDcqaf3PSsqkk03A4kZDFSOyXxHSeuGrxXjReGc6WcXths/7Cn77OLPucHR0bK+nIH1taohB3ZDlCwnciaV25zIAEsCEkYV9GORemKY9lZ2mWDkfDNdAnyy4cylLdqtLP5+Q8swHY04B9NfPkMl8wNdXU8+q1nsSiGdBXOzoyFa6VBnpguSiOehUExgNTWCUAAOtKYHmsqaS98VgOkJCdsLAuu47Hbdn0HA1YGMxJDHL6p6jLMbpvwmwp3mW5eTXMNGDWoclDdZ4G7Ad4D5r+ui9UQxhnkU026gLATVEaXtOA/XXAPJAeKW6xEhrJA9/qcaDpO129QoKcW8cBFC26vzrtii3mwSkkgk+NJ5Y0QGZamkD2n68DD4ImXC+Do9s3hC7efd7I+MSPbn/EG5/DHoiV1K0FwgQpiXjdd2iiWMz3dmU3tov5l0pFwkxQspWvViWS+AKoBABfsKN966aBoeniTx9yDoPBtQhKaC8A7IplMolwoh/5n7ijJlIZ/ak/GpclOvVSvZYr5YZHObJKFgCVxf6MQ1xyRXSaOJ63XLJBjWMq7ODwGLN7ebsjcnhyta+rnUdaR/3rJwZ3cSJ2KbiZIVLTno5sPMJAd7m9TTaOgJPwaJVENAOf1LUxbupTuDoeYFQYweIb9qNgm9Kxcbbb6O9U3Xa2Jkg06/EUCrLnuKSDJTQeZYQ3BixUyjwmfaVWFx5JEZ4jzdhEFUsGAEy4TcAzOLcoPmNtW48mYwBlSk7WM/DyW+wvtNFUiG9Ae2xU04CJY4/MQI3fKnVs/CBkzTigH+SalRYU1AoOoBKJxRN5ykaAKHzgsZDhSwCjyYUxu1lP1xqTgzCiA6XXXnZRMTdz18P7Ro1Y2XWIVRdiZrZ2TJWADXaGwpTa1Sard6I1Jm0R1jTRRgAbk7IisHClXC/lsrLv9JbODRt+cueje4+ATRwmJIcmiSR0pz2zwmnWdOIls0gnTks+8kMxsh8ThWUhkYjmaiwRbudYO9atTI5GWFSqDmHqy9OFgIpa9HZk6uXi2FSOia0A8MR0jom+3e1yDqHvrHClQMO4x6kJ2hoBkfm1Z1CgypF6JZuUFS4Yv/Vpc06acCUuVA3AWMS6vhKFaB42EKoNRkrUIYaJ6VI8XGP5j3awhB3yaj04kjlYqLTaWzUySM1bwxmGueqsPZMRVjltzOITTZqXfl2YYdgRJR5Pkrex3BqdpfNKcwdom9VTL4K9HK+Yhf+SFZ82z/2RizlxrQ/nAzBPIXhOnOC2JRxYla+rJTUJCl2IA/aOWXbDOa99fX0mkSwyjxANdl0oeWvDa9Uq5kqkG2j17Gv7mID16OHRR48oUfToQ6zvDVUKVUxvVASkpZN/5933s93m7rN3gtlgdCwaLesWiJ7I4a/JSc2EPXGjsqrkaU+4brpQ/PHPfn54hCWVoW2bN9JFEZ0X0zVKaaWW4pzX5Tpo8vhLkbKCkyUu+sMALBs3wHpZ7QNJQEM11oY1WiQprlnxZXKNBc65ksHGTlaXVvcfGbYsRscnS4V8T7sv8TUrPxkJmpwwQm8JZnbReTu2sEI0Pzna39vJAuhmBGpKdKJeUzRV7lNhALizs/PwYcwNq+ysRLUAh0TjdfaNs1Q5wh6fG2xolj1N4AP/2a5VV/pwPpWYH/REQp24FOa0QVh0cIht1FKxRIr1RVSByX9caTkc5MA8R+BWd0GRdUvAOW9xdHwCWJQvDptKgbaJ0xegrJChWDphIbZC6wA+MUFDHKYQiUWEKvZkGbhdyOn52aKtl8olFvUy2wtVG01X6sIKc7H3uCBLE55jb8D+HNXZDKLbB66lHAjeQEvZv1aF6xFlMhgWibjGsPlKRmi40n++p60JMymCZCmXZJT0eU+7fnhk5Ds33imzUdFRw2knlskk2XRAZBs/KjCMOliu9HXrIiKRrmiZMjbMz3NSUxFV3LNdAvpDyTm339k00MdWRHff8SDA25lJMqFF9mAQQYmSLegr8ZfrSI40VMXIaDAu43cJQDn3p0MtM3O+3vYUk6einCQxXpf522wmwm1WdhmZ5eQ7F3GrwRTMX3RxLwoP2uJOdzZVK05Fa6IdshiqLWFn/qxKkzANuFkn82hZ6b9Aq2CRmy1snygwAz3W3ZaksyNs4UXYU4YZdP1unEnQ7MZhGrB+LswEIAomECIznUITNS50j9gEBSsFTzjjzwzTRC2USnxyZoK2yYBO1WC4kRYfrODDtOldZiuWx83d5FnR595YRw0I50tZSAM2mgOtdy7v1sH93Ma0DkgKSFh5DnAsrknk2fMkwQVXBq1D6DUuhDEy62glp8enAeBfviJUKd12512Dk2iQqYlylP0c6POHyjn0BaTeVN0ZHBpD1mzbPKAaBHs9oFwym8rHG2KJJMWJrJRNjdgAS7ZWvuLxFxVmJu66/RfRcnljZxuQq5KZb4Rui2kLnOmwPIfsNtxtIK6XQYMOqiDZ6xZX/gvxoi3yF/I7Uk5HJlms1AbHnOFpWSTN4XcsQhI6AQylei7N1hSUC8oI2FCBJR3peEcy2pWOpWNhFjXR9ygW8l7cRahYziMPV7x1wMXlJD6huD72amrqe3SiwkGE/T3MeKOOwgD6YkaXvWOzNxgiGrzZ03KZhiR4afzkkTGnLR2nvTGIiy3D9pYinHxnZnJEsUUHLmzPJsa6Q7CCaFNTdJ/Y3AqiNNcl892osK4M+O2nY+xaayx/ZB8aSDI7tYC7H8uiBNeWcWDut9kyQoKCV4UDIl94x6ZwcEx4SlcKij7mzV3SYiWa55r9Xljr/nJYQJG9cmvsK4S52dna5Vx8zuZSMffz+x+bxmQnVjuIw+ZWVV3XGZt0Ricnmac6sDEhqIstrwqaiPrLz3VSd/kvIdjimIocE5PcE6/d2ZYMlWcm2pLRAc7JUTEKhkk2AmlVPZL4pPhjko8rP/v2QGiFXtAXEmRa1tIdceEJh+2wjfHQhDM8QY8h3NEm5zDMyUZvDebJ/tgqyBjmzk0b2L0zUS9v7ulEAwacOJ1+6cQsPSb8tFnQTTOPlp56mTEVbHytlaIPDk3UI7EN3bJZGHPeyI4oFsHwMa3LoI22SJgJ+GHmrtFUiiV2OQ0bPMNPNV1LF40VXECaxScf2RNDaQRTAeZZAMyrtmdNlchk5LRQA2A3GDr0hbF9x0LOB1Hz2Cxoo41Cm1ORE90CovEV2TQsw+bZsZpTBP6144AJgbUrLyipJRyw9QmocXS3j33l+rFD16zvtiV0zikUghgozctZgkgkhiULsZrzK0+4tFqY/K+f3sVAMIO3UXQGmYHC8B0CxjlytMJJggy8tUle9DNEa0FakpVbPUVfDZSQCrs0c9A9OnTJOSvlXHzetnS4lKiVNnRleVbRaTuys6L8KmC12pIt9VKvSrwolPYDxXUnDlG/9REXlHhFX1lyxe1SHVHJs7sjCxdGJp2xKScci3e0ZUnv5wLh3vvVbGVOGZqxiHgeqZPeWFs82tsRnhk5OnRgfzYuilzYZZ0baWX/rN06YIVWf9yFKnP6ZD0S7+/t4nwIplpRL54aiwz4sml6MAKoRDbNlaf4bZ6UC3LKDiZCw2cOrohw3FYxz+bZvFQZ21D+T0/L4WMZPX5bYJLwOdO3NFsfv62lSuIlYKOvxDYD8LzLkCDelnOjJXvzoJdYiEQL3KpywP9OV7WUIPMWckB60WjAXPlWE5zM0kJall80mkcyFRfbIMBZKyXD1Sc9bmcyVPr5wwd+sVdmXYnoBF6r6B1VbICP7dkXisW2b98u4RLIqQMi+o7RJSSAzEUDAsNLxfa43F524dn1wtTE8KENnW3MegbVCXRFauPoCtIs1UEGPw99ZQcs0LcZg30YlBwt9lLz1hR1p6ujPRyJDY3NDI1Wo7E4y3nn1ZwUF0ii6OvWyQrnSYXTidhF6dLd51+6+5yOdBIAnplAoT6GbcuhbZG42WwCNHB3j1ok3ko/4nUfPDJcq0c29/cy3zskr9d1gKWhmuwNGaoZAMMaFvjwWvAYABvIyZlUhKrj0+L7IjnL7EE4QNZYPTk1BfvQgLl1VXAKMEONZkhqmlZWl+kz5MzJSa4jtmbho6z3YNZfGdwmZ7WbH2cdsIr5YzRgI3NWnsHNGnOADy1wpz8HdHJHFQCe1UduEiLrkwXIL35imkVQoZCxRsepbEhGnnLNZf9+29iXfvhA7PEDTzmvPYMIRGerFmOR2MMPPxyJxXdu3y4yB/GKvJMZWqSfZ6YTEggBLOgIazjOIBS+8Pzt527bzKTS/u6OaCQEejeklAhr7hoBS2WapWB2jjCcGzROkkIglVPR6GfpxlxqxkAAC7S6O9tR1AZHxlHOmIbDrFp3wF+y0fznzw+7geECZLAxdA0uvv41L2U6dH+n8CSTWhX7s9ECxilirf4YsJZnXKANoaQeHRplVvOWzQMwqy7GDW0c9NLqsjkU90ybItDGgOUpfCImfbVymWZifVkNlIe2d1UiFmeLaTaj5MzFWFxmtOPMKJ3NyoamAtbMRfAwW5/LBV2Xfo+0vnqdnSIr8QoGHX0gF1RzaxEaNOvSnBNx6M2Qg9HWbIIWXZrJZZqUcDEVaY4yL82ezco1uFlrDgQAvNYcX9vyXPnLgSvRegn1i1329RvEGm1fpScT5EPloyb+IiJ7bWn3SoOsSqkQZW0qFLLdT6j20uc846cH/+vWH38/fSCz6QVP6dy9ORpP5PKycGTf0bFQON7b3SOpGdjlrDaqVivHI7pNrlSOcVZGTlkd7QomTNxqc611O+GLuqO/+sQr80cG2zKiWsAjlzGsIEK5kYBm0eeRuMhflXfyvOE5hsNkaeqMZwtdJL85j8grm0my9mViajJRSuDJppO+xZWaywbWSjLl0wtRL/0SoYHKKClcGOuMVsv1HT3yXANrYQwD4NOyJYSm9tgkN1ZxMa27mZNlMgawMXlb+iP8lCqi0jbNNdJxP/upm78X83h/sQmLaiup2DKN0yVyU+PwpLebNsBCImZU1aLlfDiW4gxFeliUxWEMvOqinkBJKhIBYRBph4DIiUPwS2wDGF7EpixtIhavRuMFTviria0JthVCzIIux2ohLCtEkKoJ+ulMa5DeR1bdiC3BxuWREBMaymxOqjMX2NhbW59Xd+OcqOISQmX4MWwSCnPMpWyuKZ2pWkUqQ7jE4kJvT7FdWrzQyqgDNdGnpG6o/kJd4FrEAWmXgTtdOSBSQ8UpC3Vi1Vyymts20MPnClAx9smPCNICRJrguF1f7QHJhZgU/TZph5yDnWzdGN7SEfqL3372y67aduChez/xb1/40k2PHmLZZSq6hzHgydLEVO6irTIddIbVOYzvch5cqMZcZ3QLjiQsRZBZWAVTAgP6a2drEuA4FG7ncFbHefkTL3zDy35ZRkF17RF/ZYM/xGE8o+Qsk0XQYT+yEBhgeNH/aVZeBJGWvAT5v1RHen5bt2zg3NnR8fEcWlsh30bfwYUyFDrkbFEOS+aQI33v+ooFNZh4ZLxVkuBxhGluWeBcGaUVjukAuxK5VIqIJ5lbe6KzJ6fY0tbovoRj1Uicja05PIONLvsYdg+ncrXUlMy/tnMCdGJgpSStUTVTwqGZK61U/lIXqY7lvSSCSFhieU7Y5kvR7CMPPnog7VTP3dSfjXAbrkXT8gmE8k5lGgMI+jjUp+NAWmwsJ1ucsmSWQRtYQPi+g4cikdBAXzfD6Ew3rtVl7Rs85Gmmu6cQix8YHo5zurMCH1QeHB5OVSOb0pK2znxBVOMoDZgp9nIaIBwvlWqcG9zBJjDdqVw8de9Rlr2B7Mxz4PBA6BYsJi3OfWtet4khDHYKwewB+k7pkY6YuzliOcvuJpCq9i459yMiSE8DhshULMUxzyPMk5dpYkyqIFZY9f8lcTKItEoc4O0E7nTmAIKAn2BYvcwPNRJ5wVeKJEPUNwtXETcashwIWHXWNSY9qfQxkgHEne3O65//1O5E6Cvf/O4/f/Zr1bbf2n1hkg06osnUQDqJ2OIXxZaIUKWqgErdYatA0IBq8qPdMwSIV/UZd3owcmqOgxUiAV2OwK1mhs2Je7xbyeSY5D6vfc/xspnzHMWms70N2yMaMCOXTNlmgwviUEchmzcqvSswQbbCVh81cskw4a5USQjRYWyTO4bapmcLeEkijYjSpUKClwzDyxnx+hI43BgVDOyRHTGisVS1HivSGdQ5ukon6Ugujj9200SEPfKotnjHuzKLXlihbACJh0bYZqWweUO3hAhxWmWdC223tAFOKqSrVJJd49AXqYfMvIN7tg6Yp5KjZ82lTXF0Mhowm3sUdXcMqbjOz2f7lmQowoQuKGYCoUu3LHkiU6kLmZMV52YkI9VKOMqIMaHYqlDJLRPNiQvBop0rK2SaFyH0KaCCQFkAwJVh7XolrnXlsZTFexfiSSVmjyhoXanTp5CnOtGP5OS1PG56BAV/V4oDAQCvFCfXdT5Rpl7JDgHueSmzP28o52Nct84kMHJjlqMKm3piz332M0rV+vd+8vPPfvpTj7v8qp6NWyO10rk7dxAVySIbKjKzRSywCCMZriOgkRGI7qr+s3I+tW6qtWpHJ9tmpjiPgZ3O0Jk2bOhsVGH2m55914i1Zr6wMFw3JkfRTKVK5bKcg4t1QqwC9CUMprldMUoxKzA6yuFZZE0r37Nvf7VcOmfndmtVXsXdO0rlxzwJIFL3wHKfm2XIQujruNQ10WhTo4vSm3Bpn2ZAt1ZLxyIpaidFywCsWcJlxFczRjPXv7JTFYaKXI7+AIMmGImbsrYYC1+NmOYx4HnjQnahXNTdRegFLSP/eXMLAleKA7Pb4UrlGuSznjjAO07EonSO6TWndJvf9UTd0mhpLM6hGyHyif4/4m6gO/Sbr3nWi5777PGjh7719S/ffOP3ZsaHzj1rIxo/Mk7EjAy8aWy7XVppp1AsRnjpRLO6rFSqlPIFKO/sUBVJ6sCbR5jTz+DX4ODa1g5tsqlAFf30g6Asm02DuFMzQrNAkWh00DtLJVsR8QQ4SZuBB46zd//BULW6g+Xk2h5c0njGFlbKL8JTiSTWXXa2Ag8VC10q/FnQNnXNTav5pFNiPckV5HxjQ9WZPGZ0mfPoLQGierrRpaw5djP0/jrsnEpyWwrM3CgD4OOgpPfYyFh8FjSZQwk52/ZePgFeHjwPXGs4sCItvDWkB6UuhQP2guOJKCfB0PNlhichzW/dlyNLyW3t4yDRRDALlQ2q8THGy2ELSGsk35Ouu/wVL37uzs19t930w2h5ZvtAr4hDUXkwHCrJTKBSZZdsGrnok1P6IuqkzqvOtnUw8McUaGZuMxPH3qnyTdFXK0ngmlffCGnisYp8C+XMJSAB1KEWYI4undInisT+azomi6bcluRF72S/b+mUjBedoeHRVDy8sdtlkZcBhUiB9oddXEhSzBfYGtqcUCjGFFFwUXYlqnVoeKCOA5gJms7lvQCHg5npTKRTchYhjpraeiH8WMU1kejK1j+0Ewl9ALanx73KtCqvPR8XgCEbGqwPYRowyU+at8elMYhwHA4EJujjMOg0eMxnlkAwywGwslGwfXUmA07RLxCy+Vnb5cieLV3OC57xuN6ujlC1FEm1b9sgu0jKRCCZzdrQqKiy1dertSGVKw1P3RcNTPT19MYPTsTCobY003oETJDNMEAq79V2TSqoK6xksGMOV2VwUggRABZY408759SjAU/P1J0+nmKhlX3KiWCqsMYRhDlpJzskK1gdPDpaLFe6O9o6My5XXCploZugIuyCsHQixII05rPJthURKEcTFmYWy4wLuzthefq6SxxbhrGkOJcvQrBVdWJyyokmsC2TrbRE7O5SNX0xwgijSAE4ImuFCfEBWJ+7OS/yB0CFXItsAGyW8HmT+BowxLgALMmXWNS8WQaBK8CBAIBXgInrOQu+MERATACYiRuhxOxpNuuZcp82kV8qGb0QCSjMzIRiyTh7XOsEH0TnFRfv6G1/6dHxmW0bdNKZgA9yTkbfZJWwOpOG5vcCmu5OQS8iHXb09/eH6g8yN7arS46gMNxSac87d0GRylH9Vjt9HToez6GH7EXFXhxWBXTBmJiJGSutyojwPISeCPnsRAqawiLAdO8h5hnHN3JStNrAm0ogZ5kpYL84M+45FblYFLuJMFOGbMnB7Le21rYxXK1ctZOyCszC0kzIjgMiOfmEtdRGtAAwz2SL7Vll0zZxjCBwtW1JBFaX6aR2ug5Ypz/PnxiyyZkqLDv3+fMLQleGAwEArwwf12cuir6ijrCBBfNfsMRhEEMimHTzPkUT0OuxBh6FQht+1auQNriaDZuJiiKLWGTosDvpdJ6/USfVihYiM7AwOyN1OCk9JWNsJl5NIHKrGUpep7SzSnW3t9VZwOM4XRnZ4tjer4GBzHkGTxQnRPdcdUexPo8pDL8758gtWV6KTEeK6zG6RZbOKsGsGJtDGintZc8JX9Yti4BRgFnCyxYZg6OT4XiiuyON1LPC1DLNDTxzNWAyJz46IpBm9mErjtZS1lU77gBqTey3ouziWK4mE71rJZJYbNTZfLEWiqGSEo1YHNPANED22ghFdFKWF83Q1kDdJnl5T47/V6Da45lsDCKUW69znrSQTXyLZo+5DRTgeTi1tkHNn8ralszXpY2GdjCn5UnD0v6mfw65hRAo/WWvhzg5OUmI3doVq5ENclhN6O7xFeG3xmdWGnt0xlxBX7Y/lO49PW+2s2D9BcLI3rpIEJ8RJkr82/Xr8UlWiaMAzPmsjMshVfmxtJcr+0O50jsSE/RVBLI68cSTWiIZT2kHL6gLbbyvq70rwzadxbMG+uiOwAFztH6bwau3Mn9tTV0T2w2ZZLcnkfqCXpl0koP/JqamIZKniUTUAC/E0T06YH8MqSRadg0YGCcfGj/v+q4HH52Yzj/+4gsoz2tGWqaYoIkm/CEcfAZZwc5cTgZ9y7IiV5JzZC+SpL0jU2E1DzYVFukSm6kV9Hs6OqLhyNR0TsSNLt4dGccEHR/o77NaiC5droSYqwUlVqbWhSXmM4XShg0bwM7x8XEim30YD8uFLe1C10hYhlmgBSowX5NDYmEDF5RjhUZ+Uk+Tt5L8VP8AFmLNqRPutcM1pxj0tabG1TqA4CUICiHWPjCn2JAGmEocC7Sju0ZHR4nGfu4k4auwJByIzQwjGpl2XeV7J7l1CQFyiqMUG2vh0Znj7EQ5Jrzw4WOHxgINVtWqLMdvcrJB/3p0xxO3QjZxpIfh/RClJk21Pqr8CQy41Z1V6/VY42XTRI0Y8N7Q5UyPDUXKhWSkntW5abDFYEOkOByQwUI5OeB4LF02AcdLQMHC9SZRz1sTShKcHu8ZRXm6SBM8GZptkBRohQ+TeUHO3o42BmYhoIkb6LyMP8tO3VYWm1RAM+KFq4+IpXK1Uq2DvGzHofQqQOqUKt1hvcasLeW2sIQTQcr1MDLHtGRZcCtZz1NLZJRJLSvORJZksTRnWqwpM97srnlSki3ctmjzPA6CWsQBaWctcfTIaNm0OWt2oCYeEBSwpDlCEshqzYWmg8fapXUSgWFLRTgTCMFjkmCTNH1XdlFnyq9maFXjqSUfYRn+GebYZJF3zH+WP3LsmdUe5uPhf0O0ycSZdepcohvUEeCHiTTnB/H8fBj25L0fZjDgqjiWfh5Z2Cji1PBJrWtOPORs63ditWImVu/vkI2xa+WiMegYYFuDSjPKO7cU/94HM/jL0h2GRWbybFfm9hW0o+BPwvITney7ICPmM4/OOOMzRY5NGOjroItm/FHFGCaalV5mKVrbsK4/sggGgp1GCrdIlURCFV+Ikv3JhTaSJBNsuFbNF0sCwBo8mcthj27LcjSzNFCJSCn80SSSzHPMXjeBhvohDwXdxZlVz/wLXd2oOgZMHG+qwzzR6QqYVORZ81uYJ2oQtIYcsHa4hgXOLgrt1iYO3HDDDS95yUsGBgbYx+f//b//R1tEW0Wpfdvb3sbm8vh7e3tpNy9/+cvJgPZKqkceeeStb30rgVdeeeXrXve6Q4cOgcp8JKb4grh+OzMPOvQZ2AEMRXVdYq2WiEf7eruRBSK13XUZ9jJ0DNh7L/4n7QWs279zmi41a/qZTo9p0f+59XCh2u5IcKq7eqXMPB8m0WbYg6MwuaGDxVlYUF1NjgqqzG/9W22iQBCal9cu4FSbnsnZa4NOUeb4eRCF196xF3Ai78rVgB1naKQ6VaimM+19HWIzaKJHzAPqIMTlG7OXgSsMttJCvJHSfLGAAAGAxcnOnrK7i7Y6Jk7LKtvpXEHic6oHNuHpQrke6mqXIXl1MgujUTcNssE0WVCXluQUVxaVWxw88sWXhcx79WthColqLvNGdNcBmx3Rz9mr2fxJgtA14MAcKbYGJbpFjI2N4dMtYJyjR4/S+J7xjGd86EMfMug1ozTa7ZEjR6666qr77rvvu9/97vDw8Je+9CUgloR0Rd/0pjfdfPPNP/nJTz7zmc/8+Mc//oM/+AOgF2UXNzExQQRrbRaZW/LcuHGjW/yZ8ofd7WTiY5q9BepitDfRU9aTzxB/pxYbTID5V5XPNODZbVjn+Gi9ROAhJk3Baop2itV60XdUi8ZkWIHhwq193alwrac9HaqVI76tY47IXzSvFXw4+5U0Zcx8LFUcQY72rKwDFkOXEmlHHUhUGwpuSqTeY1703AiL3WPxOXD4KEch9W7YiPorbUKdfQK0E8/RGZUxXZNL03k5EhgI5woystUJ0xk9AGboVWupnXpWVJFVLq8qrHb+xqdn0IA7O5M+RmoRgtnU2ivO/cuWqWgUSLbGiYSC+7psaU7UY27JX8hTa/kiAIxspFwTiWYAE4w/JrcgYI05IK2xJa6rq4tywV1wkTkIz3/+8/Fjgv71X/91rsAw7Y/PAMMyETZt2rRt2zZwGt2XL5aWhPp74403/uhHP7riiitoVX/4h3/4O7/zOw899BA6NOoySjOZm9nZPCQhT4C5JZVtZaEMsYdkwks+N10pF+3IUdWKkXu+nGz2t5LYZZWN3HElCIZDU3kbXQoXfQ1siYki4k6iBqFFLRLxaU+XVei6iyzLbKKcePecZ/zy8P6Ht2/qRauUFysMESbwhylHETmhByeBa1cFLdKKc98UoKInQ4JBbZyHUK+yeYVFkOFTIS3M5OMIkEdaN41Hr93Ku/ZCjv/Xe8MhZ//+/ZwdtPWs7VJIpRiOJDwU9rdEJrIxLML3QiwkktWAK9tiAXKxZCxuItNAlKtsXMXRjYKXM6xDIpkcTl1Hs686YRVykqnMVxbL/PzMp1uM0MtPTotyISWLi7AEWZrq8R35KgBHbNeteROglhgAQ4xpwNz6yv28SYLANeDA/A1iDQq2IsyYbH6aoLUMOmjgrnVC+QbQenfs2AGmvv/972cQF3yl03nPPfdgvgZ9ScvA8Kte9SqaIAoxT8Fj9F1/OjRDwqSSj0dHQdasauumIJEhW7ZsuOyyyy655BIRAnyBjcEivV83tB5LCAJojgyCYv+nos1izG3JFscimP/YzE/5EF4nwr7CEhvnyddve95znprBQAq/aq4ptRUVhNnS5BZ0Cl0gbFbX49AttneEnYaUOAGGFXWW7d79B9BfN5+1VVoUM6LcQuShEeCWqeHoi3RezNhm4XkO9dCtJWepLNKZkJ5cNsHWbOFSke2fJTrChwUZPGtjAr72JdzMtYp+Bf2KkgPiTqWWHA6luqnIQj+Cl3yev/K21SJIto3P+piINtJHET5zfTKOiRsErB0HZjWntStWWwxNzIwhNHTaBworMEnTB1ABUR1uSTzvec97xStesWXLFuzPjAdjar7ttttorAAw6MsgMTT39fWBsuCrTdEiIX6aGo8I+eAHP/i3f/u3eChLzUVrWct1UFY9Ui4VdnQn3/yqp/WmpHudzxXjaSR2E2LJPFW5bQpaB5QvQAJEInEWIFUfyhkzrjPwJrQRXyfl2uNGoBf/1PsbiZZyuXi2jbp0tjmlQpkZh6bZUHdvAJJqWV3XoMYUYVxvdIwI4ifE6IpbzpTkDcY5FImTiSpiD+ZprFrmMHvURHab0pnd8irIiBvXuS9VIc52tvbK4YkAqnfrdgB0XiH7X+Rr4ULYOTw6HXOqAx0YC8hU1Gii4yedLDaSY/sQhtKyCE8wEypUKVTDPMLCTzSxHdUj6UiVWyFEhk+jTCggJ27p9nA+YanKRhs8iuaZDl2uEMLcLB5zkUVL8pPaKPJx5qDciI4r1XbaYuERpyKkyoRpDrKWytS1Vvg0pUxfwKFYM57MKUzcQhiZYB5nxjV4zXQ8C5H6CKtdR5xEqMrMAOz81CMlyYVSoSZwLeVAy14BcAsimraKHwcfbCCku7vb0JeQl770pc997nOxPz/lKU9hrPeuu+666aabCAd6gW2U5oW4h8kFOGcU+b3vfS/WJwzXjDrv27dvofinaTifapwB4HbHOTvldGErY+wtjbjgvTf9ODRXP1f7wtchKyCs+QfpzbcewVoj7UzwlBvkmv1oW4So82vt5uClPTX/Uln2A8u08UbRJ7lmOOle3qa8Yr/6uugGSEHsUv1VdUIPCCTSXWDDuA2ra1HBV4JDTrytGIqBHm1JzM3F8WIlr6+pPY6Rdwp8CSdSAna8MGAH87Bc1YsHlRDIQs3kKrfyI4x87aEf6EZjqpQeLfxYzjmUq4dnxq7fxhZXcj4xObPoiD8GRXQCdPkufJNZyL3dSXbNOJIvQwm2fNByatpJpjo7Ijk+pUq16MTTTKlmLlnViWI2htUcO5iIR8ZnnOmwM5KvJ+vRgc4shPFLU3056AlgpW/EsL2gL4471uIC+7y7re2sEa7un8xNQlYdlIeKmijXipQQqXSKUkGfgBK1gVfDlTxFs8d1OZphXliHnmANNttp3xJLksuUt2y4lI5UxvOMYzszsh1WmHXAHKWlcYJLyziw2h/kYhVDSQV3gWEzj2CMAiZJwLIi9GA8RACJsUIDycS56KKLUG1/9rOf8ej8889/8MEHSY6xemhoCJ0YLLeTUMnQ1F88RCAJhm6iMQXJxoYXo+l0eyYSkE8ZbqZ1dMm+bP16/apKnNkh/qN16hH5uaBzmzRxmn8LRj+1HwB4oswhxE1RE87YxG+tPj0Pr/OxRq/YYBEq8CD7uaqrhhULoRYA4Ifgh+ZEJFwLx3LW+Ors0SFowSP5Y+m89G42krsXpI0AnDbFUAAKHVAD5cX7ydl1K+wcHHOKoejmnk467LIrq0RQ6gBydWSKZqlEyT2rd2ueBsw+lkwwZ5obs5tT4SqAh4WF4VOKg1R+JIPJSU4prNULJafAgmPOwKw5GcVPzZ5YlKBdIIrW+QpSDf7rhHBySIdB4vpMpQbkkzOx6GLUNabmoBel1gqVzo3sbSd9CFY3s+l5jLkASolkqoRZGkvOLi0ge6Um/BdtOXDrgwO8+tY47DA4HywhAtRkBJcQm5+FzZlAFFngE30XfEV/5RboJSZqMQouFmnigLuf/OQnQeirr74aCAdxwV0yJxpXItjQMh5DejyBCzgQcGBVOXBcGY/NK4GJth7iSGBxAmqLJuIhP8ADRDW5NW90CySCRBOQJGDPnhGMwuds30Ywg11ansTw+534LB2PGMAw05o7j0QfYIIGF5EtRDB5wtp6S8KVvNLJFNImVxBgn5yaIhozueQR/6VfQJTFnM2AYSiNqPzMWU/Bu5vvr2K8bi/GvA7wd0EH5SYSieGz2ReMCyYLHqwyB47TLFavdN49AEn+hpGovz/96U+xMKPyouPecccd4Cs4+pa3vIUFSOi4P/zhD3/zN38TRZbJRIQD1ddee+073/lOUt15551/9Ed/BCSfe+65RCBPf7YzmePIimwpzp+ZtXr1CnIOOBBwYC4HQCBP6/IBhgFLwzmsu9JN9rRlX8U1TZEHrpaKD+hQjV9UR/RNeSL2VRxX9UigPlIc4xiGsNjDH3vsMXZ/O3unALDAm0yM8lKoz8c8zUx2GuDxdH7asiYH5icjSYxgpVZGYs1xS/J0OsXeflMzzL1yxqemUMh1+zmlSnL3YruJ5v5BcKF7mOAyYihubqSme6kBjh2eZc6XeFFO5M8CjqdkiBrDcx+AF4gbBK8dB47TLFaVEOt/0TUDF//zP//zDW94A9N0sRK/5z3vedzjHvf+97+fmVMsN3r729/OLOg3vvGNTMVioRE7cgCoAPA3v/nN7du3X3fddS972cuuv/76D3zgA7qQjsxKZELm5EybxgNgm8dKXNVKBZkHHAg4MJsDTUJGjahgBhiDvsZhQYxFTk4p1IByTK1yjcizM3AxWPIhLatxBXgliv1lX9VySMZG+UmIhRINq3I9IgOuBw8ejDq1bZs3ksaoIVCRV3IxwMOjmCYh2RQjNg76qNzg6ixJKoLtCQiWHIC9GhowWVlu5MC+IqzwndalwCwCZq+bznb2fzW85zldB9O8NcNjLmb8A4A16jGPFw44EQDW3IDwQB4uzNc1erKY1WK1SaA7RgsATelvvvCFL3zyk5+M8ZlGbwuQGAnGtvy1r32NjptZfowehoSJj6mZJvvRj370U5/6FDEZJOYpOi5Ya/uS40HrBXcJt3aGn8DVrlSQf8CBgAM+B8A595Pz1C4+SIMlkCaTStXDucmZmZrs5aUGVzFE+6nxkIF8wp4DeuVWIVwwVKcmESDRLJ3CqiwPItSGXmeqshqC7ZoH+mSuOI6n5KC0ESA6tA+9PGXaVzojQIuoERsdMUJOvlQUDTghwIxj+hLlCbFQzfStkJMVDTg8nmdad/vE5DQPO7Pu2cyo5CgCEhunpItntjMN2A6YsSeiAc9ixewE3GkE8luKBuxvxOEzlPzDmBMC11IOtOwFMKwL0IKsoCk6KyZomyFl6AtPwFQ+AAY2eApI25o89pskPk9BX3CXJMAtMYlDhoav5GCICx5LI+b78fbWsduWMjwoPODA6cyBZshQwNHKzhb0FgfRo2PA4ZmcLgU2fCK6Pm4IpjkTkSw/D8X1Dkzxf5KatFaEgBObUE7IwtxMOsEmlKJwMCGZ8S8fdAXmQFjPqW2Xo5m4LxbFYIuDNFnjyzofNfMKbTVZCAX0SnG6+CeVjAO0MwXZTRINmBjZlJxFqMkllv70fr4Lxm00BIRYg5L5os0KU45xsUN+Fx8D5inST0Sil0UgDD1OtPJvyzRgG6zlwwCDcQaZpt3aeC2BYC2txGLSgABptsQiDo9oSeAu7RUkhn+E8G0QgUbsj4WQxNd9jcdBm2tlWwvKPjM4YGgDJJpD4guoeBowXkKAI65RNnsKhTk4SPFT1ui4aqKbkmAfLyxIFVvJUEBQVVwLt1uNLoq0LPAhJShb4BiG6UKtXk3HI6wFiAPVgugxe6riTxY1oQv6BAOrzGdCVtDvt9yJXJFZ3A0TWrMkQcWnoEQMhAvlS1XSzBSxileSsTr5g+6mcC+OrEgwyrIxWjx0A5qLMDIWuiIMeWTWvoXimHKCaJ3L0IUSBOFrwgGv4a5JYccWAliCkQaTPDXtlpZkzZEQ/xF+mwFBHJDV1imBzRbBrkTw0Zf4vuKLP3ABBwIOrAkHfCCbU1rIDgNmMqQgnJqme7q6AblSWYAukkzJTCemawhoyc91sxGDbb+qpbLgMj+W8nKCEeO/lXAtV+G8TbKpcIivPh0dGiYTBp3ue+ARsrr4gnMVDquUB4n83IxFj/RGnjUIi25bGwBYK8oyYKcgRGHEnsRi29PXC9aVagVOMiYalmeozc8UwOC2TDoSjQ1PTqM1D4+N16qV7Vs2YnqjPAZ/Ka4wU/CK9KrW9NcG0RgDlgOY1C0OqBKF8bVymbMcUDzAV6Sfl9QymHVFopIhJm6YwDJg0J11wLNiBDet4ECLAbgVVQ7KDDgQcGCNONAMCW5fWcdcDWUZl0UhLXHKrpCjiqur2nrkeem9v4BORHaJ4r4erldr5QIIHnEiiXCyrQj0ViLRVLqSLxGhp6eX44uA0MOjY6xB2tTfC/Y49VJMIF50Vq+nYHDs3bFxFfkp/pM15QBzWKhFA0axjjSWIbljzmjTUYGyeDjEBh/lWkQ1YHaULCVjYc7eluW6CvaLAyr78FIcuqxZCgDIJQ3QauylacBSa1+rblZsPF4Hf1vAgQCAW8D0oMiAA6c1Bzwwa1RS5YygBRBQB97wEZRi96dQZCqvp/gRQARFvka6OT6ZJl1DCXb03D4OV3CiyUgyxgFG/MKp9FRe1N9oMpmbnARgwTD00Uf3y0Gl5+7YJocbMAAsRz0ogvuZy9Za3g27mpArIB8KyewTdGhW98gYcBnKzcBm6AUwyxgwU6OjQDbbYMWZhEVNSMJhwBSUTdJXALvrjB6D3tFovFGKV5r/l5E0iKREHdh1kfL4O1UpAJOQfHyroZ9ns4ca4QyqNVHzw8DfMg4EANwy1gcFBxw47TjQQDIf0RporEsSQEGEDjjLzyZwTE7LqX/iAFR1cuv9txC5GjazV7RYfmW8mP0sazEnz0lHI7W7Hxll3DXeHpfTBGXHjIRTyrOfHrs2HjhyFMg8a7PsPK3bcIkyu4gGjIYM5jLOhbGabStxaOjMgmaOVTwps6BZ2yTmb6sGeB+RBUaZZIKxXsaAmX+VK5WpayaFeVhWQrkaMHegq88XybjhskkX8nU81w33FdZGvDk+dx2wmMoBYOPQnCh2awclGVT7ERakxo8ReFaZAwEArzKDg+wDDpzxHHBBx9O8FCJFA2arClkHPM2iHYBJNeBjedWMWMyfYvPFeBgtlg0vOEkYRB0uOF/+7o0f+sS/3nwPXmdoxpkqODE2PE9EQNnJnDOVy2fa0rJ2gt0uBXkdBoHJVTIWlVp9WopMhtb+ArQxpMokEnbpkue6DpjoNg1FjMMozRqZ3XKJgTebSfEXbX6aMmpOMhHXIw0xS3u2ZKmkFkNGxzi6HqjXaKi2L5jZqxcxFLvYzHJjma0tm0M3T385JnueShgATP0gJHDrhAMBAK+TFxGQEXDgNORAQ/31KmfIgVYooKVLGKamc240VZGJKDDlrj6yScuaWMGrWhOdtGJbb0RFwZ2oOrfc89ADh4b+6bNffOSok8jKXGfRgzWfvQdzbDfN6gkC5EgiUUflwp95wJAgTchF1kOG3W0ywc1CUdcBu6e/hDi0SQogE8PUWp26gJcTkzOTYHaYPepTKckKeBaNX2AfN0+R9kCunBzDdWpK9tLCFl8joceQRqRmnxZNZJs7vTgAowFDqgFwcx6Bv7UcCAC4tfwPSg84cEZxQMZEqbDBVob9k0MRNpkiRABNlgsrAC7EEh4y9ylULzAnSjMiZT7sHM2V2jeddd++Q1/5zg1YgLEQz5Qq+TJLkJz7H3o4lkiyZZ5k7W6/IbOaG07JaUCjlk8YAAz+5Qq6UTVjwKUKq4x8kPNXWOgELKivtHGgrxMam5xiDXAoHGnLyC4czIHmQtXc5beeDaBRepMPAKZEO5CGYMDyOEPixkQPgLGZN2U21+uPAUPMoiyemzC4X1UOBAC8quwNMg84cEZxwDugwJPyTfIFsS/4qfZa0ca4ibM0xqmUy3kBQZxopsxpNk2RpHglAw4jIrIibjgSS5FPpcbaI1nLxHVixpnIleul2sDGLT+688HP/fABMNiJR+PJdsDmwIFDiVhs84ZeyTbMMmCZxsUGGVhkJWtGb8NMBGNuc5hSJISRYvWkIhxXHrPjilnuVK5hvq4lwlUtNFSVYWB18lc6D7KpiFPLFdhWBPqj6ZgowIqhcviDHEKEs+lnWrSkI4ZuUSIR0ICT0Wo4PlGSaVyQwObV3j5fmpYoIaERAoQGsSGQKAy1JTa/jkTiOjStUeWB5C+5CubLLf/1aEYPgIVmNUNIvCU6XhO0qTbPoi+pkvfiJJDOUI7VVn4EiStxArcIB+S1BC7gQMCBgAMrxAFEiqzQEaGvPzzcCtSBC6FIOM5+GMwcDtVLlZ72UDxcnJoaIYKcrFCccWqFSL0CzuQ4/MBwhPMDapVquZ7Xw+TrRcZf5UTdsFPowIjtOIcOTbHY57pzdj3zmidMxDo++l8/vi8vSDCeq1L0gf2DkyMTl+3aqLWLO5GUHNaQn8hAA/gQ4pS+VJ0tOsTYi3aOZbnCH7LtTcdq1ejIdIUJTmQ0NjOVTIT6kuF4CaCJxhLZKrt7YA/X8wM5DDCeYn1tIduZ2refDOIbu/qlxEQ7C5MS7LQZcWR0WHanlmAQVAFSgBbowt9JiUlnspI4UkpCPDO4iSNzttVmDjs1phwskaAfgA0egsMxGdINOeMlegdAvvQjxNVF3SVPCuO4YjLDz+SxELnFErmSBHEqskTUnyRZmjOUpSeiOCv7lwmM61QusrvviPPVX4yNyXnDBJdLpUmuFmVp2Z+JsbQ5nIkVD+occCDgwCpyAEz1f14xSBv7ySN0shizqWIxNsJCVHN2AhN5ARXgCXDJVR2W9BSZhcxannA9HguxJpdtnGTSUc2Jopxiaa5XmHk8Nj4eDkU7YuGXPue8rv7+Siz98X/9DoK/VI/MlGULyY5sJht3koIUBkyckIQuKao2ChrIQWTdiQN8KVMWJRAvITpmpFSVsWgwuFgDrJ1UqKZKtAAP0WT2s0lQ4JGp1/EwK4mwQbMJfSImZxGSf41TeonKLwz5AljqMC/LQ7snJtVORekQxKYrUejAyfQwF33llpiEk42gspBsuQp+l6oCz+wqBjniyI6jgoVbGl8rRxiYDa4zQQy/xNA8xbtkZzQoea4GbCRhnOAI5H/5zFf/9cvfemCEKgtTwraYulHlJRdzJkW05nMm1Tioa8CBgAMt4oA7ZUlLBw/Q8ibzlUo4jc7HWGshlKk4yZm6M1V14mndsCMW93TFCmOqss4GbGSprmB3rMbSXNYgHToMau7YvikZcX739U8NTY8P7d/7mS/9PJlxHtg7zgzhjX19mZRXYRmMBfYbUDVHAio4SWQb7mVhLqgjC49RxMMcsMaYr5cWEzU9BR3WBUg5HCmVSBJ/ZHSYfT+yehgw+fjDvkSXomc7/x5PRjextyOBiaWZz47dfKcDwBIH8pawDph62UJhPZCwiazmPI/npzDykfrLTtiuA33pSjAt7ta7737w4Uf27B1XDnHsclKmiB9TZS9d8Fc40OBjwI+AAwEHAg6sNgd8DAY8EOWdXb3RdOfhUWca02UoXApHEOXViMxtPjIpcMtUYHbeKBfymF4BXxH/YvMUKK6HY2hg+w4eqTrVzVt6CzPOeRnnba95RW1q6jvf/vbeYeexwaFipbh10wZgXJDVB8OFK+nPe2LFEbOa2eWRuOjhbJcJwbKYRzFPMvMzoUpooDpvq1ouDR8dwjbNjCooJY6Y40VvVUD1k8wneG0eNZOwLEUI1f94ztYpgfpEXHwjDj+CHZ3k1+J4Jcx6DmFUSjBDauT+5RWgj3PiRaq9s6tvw54Dh1VRFktGM07Pyii48Thw/HfsxQz+BhwIOBBw4KQ4AGAIWqG4eliIYblSD49MO9Vu2dT54HD1llvveXTfIeYybd7Q/RsveyInCMYi0VCFMeFypVCIJjOi+1V184uIGIcHR0bYlRItNxMRI+3zLm0/sOu8X/zs5k986rOZ7h5GjLdt2QRWgAriopiuUdgEPPgPophH/7pehUt3X3qOfiGhrrPF4ByRgVngnx97gVhiEnmeNCt/RwoTY+MU2tnezhOykof6B/wmKz1E2FIIDTgiGCWdbVlu2a7ZfSBIb7RovPkupgEbAPsztOeLKGESIZcHgC1TqQWhHvELpWoOhyAYAAYbA/lDDgbAt97/WD2RikSj9z/8SMnZTWC9EooKtxvz8pqzCvzGgQCAg5YQcCDgwNpxoBmDEf7bBvrGHj70ta/e8O3i+NCeByulQpn9rLr7Dh48uLmv53nPfmKyA3NzVA7zqVfAYYeDBMUf0tOJnMExZzqPvTfR2xPPCMiGB6edN7/qV/7k8KFb7ryHIWFAc8fmDWCGLAhypR0qNpOTZMYSiEIayFAYkgVOBHKhe4AGzLQxNGCCyuUKBIhTPnGkkJzv6zuxRUtWqXgiGg4VctOZSKStPUMMGYa1iALYjNRK/l6xfnrx8AANGLCamZriVhKSJX2FpnIkXpPTboBQDADD1SUBMDt6yXoqYYT0YxbOvKmcWV44oEywlJjeZX433aBDI2O5WnWmWKM/BPoSWJwudHVIlyJwi3CAZhO4gAMBBwIOrC4H0Hr9Anz1F1EenhkPTw8fPfDo+PAgY5ldPX2XXHLJU5761J3n7pouVY5OlDEBY+GsygBqnXVLTq2IlwFXJiODnfsOs11zqKurXQExFy9WehxnoM15+UtfmmzvHM/lI6Hqlv6sgK1bPvlwfi97Pbui71gJaFuA+GPApCtXK5EY62xdALe66HQwzkziubvXFUZgXLlcBIYzKZmERaUlfy1aay3ToHw++B5iAs4o0BgI0LkJB8MEHXVumB+t2SNmBGWpkKfjuj55zdGa/RYBJlvC5kdL9EMRE9SF7wLhVASfqwGP5TkLo8r09HKlRh8I+uMsGNOKLzHzMzOa26TOzMoHtQ44EHCgJRwwDOC87tc+/2nbb7kjm3AuPG/n9i19GzpEeUJ8HzoyuG//Y0dGxrZt3oAqGqnLwQZOpchxR6L/gjqsAAo5g6PjDCW3d2TZq8rJj8XiGzoiUab+XHFxx+WXX/6Lu+6MFqZ62m3gWCsKXgK9ngGcfHANSCRjD5hj4Qj5s+EHICOHFMmOz6CkgCpOMuBGquGCJNGwlMej4ZIeuJTEJzkT7NtgLY0F0WEQ9OKK8qw+ZnpLP8HsyYKtRLcfoYs62xXE088XjEoEOgF2HgOR3CIWjD7fA6myhUuloI468rLoIe07cngyl+/u7cxP5R/eM3XWRW3taV7UfJkEYU0cCAC4iRmBN+BAwIHV4YCv9Vr2NnjJlKZrdiav2Hk1ohxn0hy1GAzeOtDbnm376a23X//4Z7BSKMvqYRbOsP63UjVDcColi31HJqZJePZZ2xlKdpKs4yk6HL+bipPJs572hGgievnWDcWJSqQH3QxNsco5vpIPplM5zUFKbDgfeiPOdMnp6uqC5uHhYbKamJxEy+zo0KW9HDtYKMfpBjChGlXXVgrpMiSONy7ff7CGDujUtg7ITl2yoApHFuoMj6yyGqDwrGSUS+VNm3pQT8dkJS0E1svVKmf4WsJjr4BuOBoFTVl+pDtI59t11PnYmBZCSWjn+JkWHglnnGI5kvCnhi+UaP5wKAtx/hNjABEqT51CM46zZ9+hZCZ79o5tj9499djBw9O7ZRIae297a5PnzyoIDQA4aAMBBwIOtIYDAFNGdSq0KAQ5IIE8wg9EDfR2ssfF+AQHK8iuVUByXOAS4DYUk42xULwODx91yvX+3r4YK2nFdgwiVGq1OCt02Xtjy0uuTeWcjRlBQBKr3iqqmyi6WuMmLJzFATAeEzSFYXzmwUxB9upiJNrKdjsTrhbcyCgqSq8sgo1HfeSkKJTkZiXYrSlRtXTUbAlBO+UkYfBclGqpZyjEkyaNfBZ9pPWUeMIBY25RcI28OTHtllJMRXY14Kbk88ZfJFCKDrGomYXc7FcN0bGJSaoQyWbatm3e/Midt4+OTbIkmZchXaWFWLxIAWfSI5pB4AIOBBwIONACDqA+RmrFaK2YYHtI3ZoKHQ245Xf2FjaPLKOA2taMss5Gt72I+otz2MQSxevAXvay3DHQLwuT5NSDKAfzyvImpkQ5Tl/I6c+4E3cFCMyGLEO2JvcUsAUp7MekJwnnP8+ZhAUAYxCmQ2DjsglON1Rn6rt4da6UBZJ/MsEel7LdZCYlHroKs42wkjklHesklTi5ApA5QA3IloHqY+POCgELAV1bB7z0MWDJ4sQAGHr4RcN0DNj4mtqEquwo4hw9Og0d/T29u8/bwmy3A4cOUQKEeTaFWTQHN80csIbYHBL4Aw4EHAg4sDYcQGFlp0l+KLSyYyI/w+CB7nAmGZuamhifEp242FDuwrVaFQAAbdGAB4eOJMPRgR7QICrxwDcmG7PTBjbhkpNgQw8CMAnL/hsAryItGinpF3KKqcATGjDKKAZbUhbLJdRRO4uQdAKVOM0DzdPPSQ5QYrGyHoXEQ4Ufg3Y/SkPezqZAomHVTsZj9Xp1clLybtZx/fRzPAbA3izoRuZzonFLhmaCtjFmQfgTc0Cvcr6uO3zFtc9y9OBgpB7etKH/7C28wdqRo0N0mHgb3pFWJ1bSGZHqRF/DGcGcoJIBBwIOrDIHBAv5AWNiXEYeRZwKhmj01/7ejrpTHRrKEc5eT+CmaFVMWcLGrPJ9osIpgVPd2bZ2YJak8TanHmNaMtI/GnJ6Y04H20PWHYZ9mUFlko4pVaUqRl5K1AAZxNTf7FpSAMfbV1BGiwXimYqZFHxFs6uztEk85CFX+yPeTIYIHEXIOcdZQlEKG8/kuesgvslRJQ1AXQ65i48nOF9CQkltVDdF97wGz1xJzPg0Hjvx13s+z180eKK5ACwLsueJs3gQKdg3FGTF9iDGgjq9DXDYOXrocKTmbNnQ3x112tOpmZmp0UmN4/aJFs/1jH664As+o7kSVD7gQMCBNeIAApzdnmVPY9NgketgMIC8fctALBLev38/hIhJln+KVjp9SrwHj44yVXlT7wYOHmI1kijPiUQ1GsnpWUmy8reCbs0OHm7GKLcAcJS51y4wGhTOBSIDTvAMDdjWARtomQZMoJtciJG0NmqLR04whAjRgDPHCla6DxytMLcwkpkLCaKRA+r1lC4F9h4c5y8ZQh5WcSF40bizNGAhe9HYCzys6KwzbA+uU2v+4MHDmMu3buylI7R5Yx/83bNvWLM/oTK8vM+Ev8e2kzOh1kEdAw4EHFgPHABXmTyVYCARmQ5OmsBWpHXO2roFAN6z51EIlWnNPBQIY9IPVmpRG/fsexRldNOGPpm6JSpyDOso21hGwjE5OamUc/JTDguJmRrNmYeM5lbraMOWVv4c6wBmxSVU3GRSjMBgG8jujwFTqAu3TcjiAzAbcZAlfYVM0h0t9iokfw3tiUBS+3nl6x17g6D3p1JA6cyMaMCLAyS0mUGAaKagW9W8POf5awCMumy0L57/POmV56S17oxkQhYMB1Sc4SODaMBnbRI7xo6tW7DeP/TQQ5qDFTVvZkGgcCAA4KAdBBwIONAiDmAY5ifarSuJQClZ3eJg2Kxs7GqrhpMHhybAZsVNRQAwVqy8AmcHOSwwmupqlxMb2pKgc7WI8itARoac18CYKtZnfjGGbYEfzkCkIH7FkqlwJv0IaHKKSyA8i49Z2FushfOObLZFjHjYTj+SGDJ3WRzma0avxRGYjIQS1UrcqXB2k0C1POC/b4mmONkvRDKWB82yl3Fqic0hxImwM1OtYkVX56K24Zgl1DyNH5gN9IilWpXp09AsWZhTn3crtSZBXAqJFJy4GAtCzNiW05O8OI1uQRNmUrr9Gk/ZxIRUMFzPeAKCo5N15+hUKRKqDXTIoYdberKRWGL/kWFYxmKvptw82oK/TRxobgRNwYE34EDAgYADq8wBpLMcvYBQr5fj9aKM5MquDmFOt42Vc4/ftXMo5+wdLglwyROsnwzqJlgB69TyHC9418MT+Vj3jnN6QmUnIqFOOhHG+AtC6OGGcVGco3FVslnWIzhkWaXYUUucwr91AqwECGIklqHNmmBMmZOP2nr3D0NSHBNrR4wjmnB14Fjm/kohVc4qLs5McwtSbe50otOT9amRHVs2AHIG8pKCh9LJAL/5caREzRZMgWKAMx0GcDQUSxSKzq7NvbX8+FA+jwosO4Ao1kOUdiqoVy0km1MCppgNZDNOVkhPFJm6FXfKHFYBg+ieaHFCjlQJVrDrJh7G1NvC1UItmo91kjn7b0cjGAvKstulWtJJoJRISkVNf3RcsuI/1SECj9iNLFOdiJbHOXEwF4v/aK8zmezZuqW7g+XadefKXVtncsUHDowTs+LASKVIqArcPBwIuDMPU4KggAMBB9aAAyb0RdzXGakt6rl2IugJAdE4fLCrd6ASjg+OuFI8FOX8X9XaVE+dzIcK9VhnZzwFnspsatlhw0ZwybkErAtWgUFIOVfQgZv282pnjzwxCDBKAbVwSPTaSCLJFpSFqpMvVpghloiEAEwi8EhigUoCpNVkPMGffNWBuA1t2Uw0VCvLxDECpVwmeQnIuk73xtJS9DHQS0wDNmLInl9OOVdhXrgq2VKM5ERc9UpEi48ZQK3QclQiEdD2qac3y9vKoreiZegd/YlEmA5MNF+T3ZtrGBHISdBXMl7YmZ278RxOqfmBfgAM57U5ByadUizV09WepQMUqrQlQmxqnSvW6Dw1zV1v5BD4mjngtbzmsMAfcCDgQMCBteIAeKaw6JbniiRVWLdt3QSuPPTwAZ4xgqvmT513XHOmCuXc9BSG0N7edjeD2dDqZrfcP4Cdd1iTLiti66iyPwZsU7bdJTzuXCxolynWYGciCuazQ2app7vzGFpEQdeaLkgQM7cZA2ZE2YrTUhZPIVnZ+b42vqs8mz9/MmKhMBEYMAZym/IVr91y9T2zc3FXiPEu6NG4LhQplkVLP3RgH0i/eaDfwlOpxIa+3kqpePDgJHPUFod3L68z928AwGfuuw9qHnCgtRyYjUnciTgyDDCs3bkFAK48+PCjqHkVnXIsBBMrEhsenyoWcm2JWDfGaNcBB67O5+blPVjqX4ELK18U03QyEULJK1XyxRIYk0rGJXfGUQEiiYixGoRhYaysMgaZ+E2NDeenJjZ0d0KAZuTSI9HtYAZN6l/cwjQyMJ7JCAAzCYv6unzwox7joXzi2FGJtkmIBhwTzwtoPl5CMlcrgjz0tx9R1so7aDilgurJpiKmZDPfDb2ZoviJReDAPvZCKW0a2GiJ2AN7y8Y+Jr4dOHTE7ak0cgt8czkwm9tznwb3AQcCDgQcWH0OiA7pIo7nk6nQZw30Yc89ePgIIt+JMJorQ6lgWj2cOHx0jG2YBno6iC+KFqm9n5cDafAu0zUQx0kl4rVaJV8q5wslls1mU5abaL4+eEmhzMrWRbuosDu2bNy2qS/NLpZu2SCU0Of3DOajhgoJTjP6jPEWAJ6amSaFEKLEzJdEwoQOAWCZsAUAk8XSAVhSSu9ByRO/FOX/hGBBZd0aDI/RTxyNLmc/SNHhaCJFwPDQYKha2jywkTBsB9Ri2ybOf6ztP3SEboRULHALc6BhUVg4TvAk4EDAgYADq8KBBkLKcblyhxy3QPBh68ZMKlIfGZtgKrIurZUnbGxFjL2Hj7Lf47ZNG9HMqrU6A8bi7OrloEEncGHYVvaRSKc4AaJaKJZzBZkIlmKiNZkpCAkUCS1MIWOqrywJ4q4z7rzyZS/K5ab7Ol1lUctWctUnECUegzQN0qFeCSKHsJP1NGCiaUyLM98V7FQEZq8uHgPAx4nP1GXdqoOFVW5MyQGqpRri5kmv09PlkQ5ac5UZ62HZBJq/oSj4OjTtFHIz7Fk20O+yHvPAWRt74uH6vgOH2Mdbp61p/sFlPg4EADwfV4KwgAMBB9aKA4oAHgx4ehgiHhm/dYOTjoVmCqXDo05Xt8zWxeJbZBtiAPjQELOKdrLtA7ojcQ0/UJs9NZB1Ps2zkJZcG5YaC7ySbTaVZM5UsQwAM0Es5i7ulXFgT3XEy/pd2RmEFbGysueyXX1sQY1UpVvgzreaXTAgRlVViW5+IKoogalU1B8D1sfEXUCH1BTEsW2wGANeIF6jFNssWtYBW/XcPw3Ou1HtaSMdlFEVspcS9GDGOOPcTrnGiqv9hyeY/tbT2ZGJUF+qICu1Nm/IcB7V4Mj4eN7pYvp14BbmwDHcXzhq8CTgQMCBgAMrywFBM3McsKMeRJJAVz0EfnaEAOB6sVwfnpDZvraFBFN/UPqGxnLxem1jR5ocgAxxMidZ/+KVTHCamQYu9QKg6vm65JCQM33r7MTBGDAziOUIPnKxkxwEvXBhJ8GGHYCqrN+lMKAXXRa0Aoe0agsToHQrkTo/SwdZdYsRpyoHGnqV0mLmubgEGLGMmEe8gHniWpCdIaGVmy+OqLn6U7rpZrC2ip+7zFl0ZTFZu4cpOZFSlYOQnKHhcboMHVnO/nUJJl53m5OJhabzxRwTpecrKgjzOaANwL8LPAEHAg4EHFgrDiDqEUCip4p8txFGwS3BpgorhOVght07toK7D+07LPOSQOY6k6HCAPC+I6PVQv7Cnd0yJQhnyVTnJaFmSy78XabjDCLvyEO2dK5VK0eGR1i+Wy4V21OKMVEQh/0mKY/8uaKoi0LLD2r5JVFkPdutlj3rhpA5S2OVRM4TlLhdXUBpaJLjh108W5j+CCufBd6Y1QwoYl6OA5ay6eaCDi0Z9ZrNNTHdy37YbFFCqVqul4YXIYhJtSBghuXOjjM+I1dlL8kqoLiY20PheCrL4PPRkXEG6XefvY040Aq6MybdEXG2bRmoR+L3PjxKP8XDbHfHLjLzQ7xyz9y/C7/gM5cnQc0DDgQcWCMOKGIi9MGBhgaMJA/pto7gGTsMh2OJg0PjDCiK1sm8ZMcZrTi5cogp0NmY7HblOjEEAyyyp9QsWPGeL+Gvq7CZesu4MmhT0H2z2AYLzFUCPHS3v4KcIkX5jxZoxmc8nv3ZaHHjEM3GuYUSK0P+6CZYSjMJ4wA8+IceLpEWc4bZBmam3S4WG/J09rbF9xcsUbz0InACvfBPvMA4S3jDMYflzDV29go5U2w+IoP0qP8GGfijgPTR4ZE6U+F6u1hnLJUMh2OslnacTX2dxUr96NhUcy38Qr1MpKwz3AUAfIY3gKD6AQdayAHksyuifWSS7ThUk+Owd0T5udu3hCIxNGB0LEUHGY08MOTkK05vh5yDRBwNF1HmiXsx6p6gIy9BGskzGccCXZ2cmmGiVSIWlQPovUz1SAIAB9ChfCnNMBYExVBNxMbwM2q76PfzU0QqhXU3YzBVj3wI59iYxDL1Spz3L8kZ0+WRTbDyEW7eyDYGbBtHk9B3fqUEgzFDoOhSfUC37vzr577/7RvvQtOtYRynB8Kmn2qEIC1xeCN7Dhxmc67tWzbCBXN2PtK5O7ZxguOj+w8LO0MhiEQ5do9x1BAv+pn+d/5mcaZzJah/wIGAA2vEAQM7UX8FcsSBATIGjL006dS29HezTeOBwSEZF+UZTgAYiR7Z0N3FGmAwTzDZc9hlXfzzsvOeLO2v2mAtP7Z4ZL+o8alp1vmmQGAFVcvVU9ybzMuCXOCu7EqpTykOIJefoK8iHmndajYA0IqivhKFurAXBylzOdkuY3Fn9mNmNYNwADDx5xiU5yQXK7UeL6ExhZVE8OhR6NUEICs/QPe+R6e//t0fff07P9g/KtqwdoAEL0SHDsnpVcWac3RoOB6L9PfZxGhJL3PnKuWzt9Ftij6iAIy+69ucFxyB1qLPwEsAwGfgSw+qHHBg3XFgFt7IDcfuohyWO9KRZCozOVOezJkCKrixf3CkHktt6u0NsSViU1W8TPhrwNb0bDleS88yJJTh8YkpJoSlkuxzJV0EsRdLMQKZ/JWfTwGTmLwwYkhktatbtFnlE+SnsgfUVbGaowzpY0xPT8+Kf+yNwifBtgwJvZmyjwvAxJ+9DMmItNzJABiWELAW2/FD+47U49lDY9N3PvAoWjbj0maE0J6Q9FMmZ5ieVu5ub+MkDAMSF1+rpY29yWSmfXB4bIKRA7V+m9nZR2IrMrgGABy0gYADAQdayAEkOUJfBBGiX5zOxXVFeb3CPs99/Rs59+Dg4RmJqkfQ7zs8HI2mN2/sj6CMSZgmFACR9I2/duc+XNoflwiJzFaU0XBkcnqGU+eTSc5jUNssD7zi0PcoEV3QG0YFk3UisRYF+pIZPyIQjThydYnTGM0XLZeMbf9LNsPSAGIsIKJnAzDarda9Oce5ftuuchYAG2FKlcTWekEtv1jYufvhR2qx5GSpevMddwv0ynYj8jNDN1aB/YcL4SgrgDfYADAZ6Furc9pEKipvrVCpHzwwQrhZv70IYsbAHzg4sMDbDXgTcCDgQMCBNeCAJ4oNP1QeiZeDiKTwaonlPWeddRb3Dz38CMDAYh/AYP/hwXokuqm/HxuxoJzn8PJTHGkK9Z4u/a8Rw+4WDFsybZiTg+JRIEng1XVaDGbjWcVIoE0kJp5UhT/2mxXNy8P7SxT3OZTLlho6V7kR6sWb9dfDMH8M2AuYFav5JiY1kDHj+YihNNfxlB9MvveBhzkWIpHtuPuBhw9OSn3gP49icvJwmGlij+3dH47GN250B4B5YYasYT32cQPhscRje/eQr2E2ngB6XS57f+SVBC7gQMCBgAOt4YDs3CBYiyRyhREo5IslOVbQ2d7TnqyV9hw5yuojtr0AQEZGRzkNKNPVSVISu/OnQWlHjg1w9y0mn/mgZtFqgu8yXksmTMDORNhgK1SqRmuhJEgsuO6N7s5BXjdPKdHq4VbA/nCVtOrwuM/0FlI5ZomfxNByk5FahOlnZQ4bFPCmxsyJZiYyByQrLEoQ1SqFI5iFCcKom6gVU3XZXgNDggekFCLlzCpXAtBR3T21BbBrMqeZymo0vLGqI1uK8ds/4gyPjW7a2HfJxReOTs7c+cB+Zb5s/GV58hYOHx0KxSIdnW2kpBuk4ZyOIYcuMirf25lKJEN7R0enjG6K4+WhOHNeRYjU/CBfK8Mjfm5fRcLOHCfvpCWOfiXl0iHCJGIETE3xpsTZIzsSxMYMcrmcP3jAU1Ixl8+m81kSQnD4iWaDImRrk+5ocH4RFseSBNeAAwEHWssBAKPkREsKFezrD+YhxDm/lkP6yrLZBkI9A85csTG9MTp90513IyCIPDg0EwmHmGqV3QgOkaQWr7FVpSAZqhn2z5rmIAJdhog9SFpKVWXGMuuAY5yty1SonT2R3OhYONmdd1JtbW1sBcI8LFylAm3imAQNevGTYPkP9lAJfoS52Obe6HMBKn3mhMihCG2cfJx3EkUnVmIbZT3ofqA9wQaYxVxxeEoBqphzKvwmneq0HLnISb56iGAx7MxwBiI8qlbSpfFuJ8/iYza+0BK48oAp3LJBGCWSMxPTkmxgVS+GIwLt/JwotWTNEyaFutzXIwUnKkK5WuHBvXc/mE2nzt3cffUl58dSqe/++BdEycNZSpmWwxYTMefuRx4scg7S1s3kTxIqFItEcjysR9lC7PJz+suFwdsPHxx1HPa2rkBrmQgppzQdcnIFZ7zslIslFg4rPgvlvCkKWc77ItEp7loGwMwaAA5BVkYv4OENN9zw+te/fufOne3t7V//+td9rgKiv/mbv3n11Vdv2LDhwgsvvPPOO0loCA3WvupVr8K40dvb+8Y3vnFkZIRUDPVjwwHLydYwmxCd2S9ZBgDsMzbwBBxoOQeQtfyk48x/tecij5DBqveqII5gY3b6kk57rFYOhcZVVk9N5th8ubOzXbBX6lB2IvwlqeCNZmcXy1diLNGRmj2sIAAgBziZYq36oOiFCCJUYMoQaoklcCse/rpeuVUa3KuEN//kuTqNj/InmENuFIcP+CUx5aYTERYCT0xMo8sLzsczTiihc6vB0EhVNGPpU4CUov5CQa0WrRSiNRRKJxrzyGvagQQrOTnjsA+EZWq5zBmXWsjIucy5ClMJtJeaZMgj01tuvf12PJfsOmf32ZvofAyOjDw8qH0K2JJM0kNijHqqkIvGI+3tWfLXSkkpLNdCA+a2ryOWjlcfHTw4RtEhh2ApgIITKe1KpCepD6dWRJ3qlNaEud9FOhVnlrNX04I6o6eCnUz5o+zx8XEw9frrr3/3u98NdvLI1F+GK/7sz/7s+9///u/93u9985vfvOaaa1796lcPDw8zU58IL33pS3/4wx/ed999H/vYx/bs2fOWt7xlaGjIBhtoMWQLAPtaMs2UJKYct6C2QZEBBwIOzMcBBQEV364IBwLERcIcratTnENOe7vT3tbJafJM6AFyjg6NhGvVgQ29YKS45kVIct8s03xckAdLcf58LlIyDC2wK0Goj1FRadVJiAz+2hKjpeQ6K46An+fImlzBJozOiCuCOzo6EqnUTL7cnpa1QBOh6Ew4m0t0lWLtlWimGs/AIAAbqBYKpfY1JmmXFD4Fj72cj/0L/VQHBMaM7NMgHnKRdFWrDwA/XnQeeHRvNJ7YvWv71m7n/B1bJ8ZGb7n1TuijoHBUDN1DI1P56alsMjHA2YPAN8+gqSZHQwmoO85Ab7g9nUSfHhrJiY5FSQrAOScx7XQdcTLfu/Wh8ZyQGSEJaeV4pbRPmDw4A5zXpta8quiplGlKamdn5zOf+cy3vvWtKLIEotECsTim7f3VX/3Vi170ote97nUXX3zxBz/4wQcffPDLX/4ycR577LGvfvWr3/jGN5igARK//OUvJ9zyHB0dJQLtDN2XBkcRoDt+Muzv7+dR4AIOBBxYDxxA+hgCCTGzRS/6cEkBGN2MTbH6+voY9N27bwjRf3DwqFMtcuSOAIc44pITT9SRY5NCZmFLv1ou0CLwFmFfKoSICBNkC8VZuPXyl56nH5PM7SewC4rXZcMQsuWaiqFM8lcmYU1OTD+yZy8x2fDraM0ZDzmTIWew7jyWcwZrDof87ZtyHplwHpt07jvMeUTFeLYz1dZJfNGCF3bYz5mNDGDDV4vIFTCWqgpdFRtABiX3DU06kSTaUU+bWAKuuvQihqXve/ARNPKybtNB7OHhUcC1uz2b4ZwKXbxELrKBimZNtqhWPR3t7ZnM4JFDUjFKYXMsdrikXo7zj9/40T9+7r9+eMutUrIUXMsX0erBYYl75jjpeLTWAZDAJC0DbRVVGGLwAJk4tGEMy7/xG79BIO0Sd9lll6EQE3LXXXcB29u2bTPz8hve8IZ3vOMd3/rWt371V3+1u7ubQV9M0P7OLxRBsyO3hx9+uLWVDUoPOBBwoJkDiFtFTDfMn+SEIA4Bq4xOItNjzqYtWyOP7tt74ED58X0HB4c4AX7zhk4vIXmI1JdbQeKTdYogkgmzhpEhEdl0osbZwH7WCsB+rOUVB9TgIoI0MvcFmu3HwC71RXXsaGc/6CiWcFFzdebVkOM8/Jhzy8/vueOee0fGxp1YIpbKjk9OR0PhjnRi5NCjmztSiYzYgdnk2SdSy2lc2EObilCder0sAAyuquFZFFco0K3HdPg8jFJ6/54j0XR7T+8GlCQIvezCszqyqf2HBh8+6ly2QfRYfoNHhzOxcH9nOx0Ifq6zytTkzAY6FFs29O4bOnDo4JHKrnPYqtppj9YSzsGK89WfH/zy92/riaaiqbZCoabHTFUnZ6Y7E2n408jNy/U0/tsyAMYgjEpqGiq4i7JLhwtMJeTo0aPAKg7DMi2mq6uLCDxij/Kzzz4bXZb38Ytf/ILp72Zq5ike1s+Br6TiqUy1104r2vBHPvIR1GgWtlMEWZ3G7zKoWsCBU44DIK58z8CZIRpi2nMRlrt4MEdXOxK5af+BQyXn8YcHx+Kh6tbeFJKa6HpskhePoVQfhOSZl9dy/qJz24GE5B9nSBMSa3V2wvLz883Uy8nVjQvASBXJgnorqW4tyqUQQ7ihKPpDIpmOJzKTVadYcm65Y/qmn93+wH33kAKdAku1TCmtVzvTyXql3JUOb9y59bzNfds39iLK4aRkvoDjEdLVNGCZCh0JwS1TWUNYv6MgcaXkxNlw4+5HDkwWSk8+91xoI9uBrHPe9i033r33F3c+dsnTdhBILfYdOJAMR7b1S7nkLEciy6vQYXiZxl2NOpFztm//1i0/GxrPMTNaHEdJhp3v/3jPP/7H5/q3bH3h05/0S5dvlA1ExNRRS3H6sqrDC5B/ega3DIBtkhQNAsewh1l1AE7aByZomE07w84MaoK46L4gKPOzuAKxDOXyQdpoMY/AZuKTA/gK7jKxCywnKwJpze95z3sYQiYVGR44cGD37t2EBy7gQMCBlnNAUUjmMbmu4QM6GWqMgA/YgQGLs7a0o6gdPnL0SNE5PDqWSbZv7nNVJT0eEBzBMTdqIQ3QLWFZf8gL6QRMoSsm4roOGLKaiFxWbkQWjJrdKxDoMp2P6olNuh6PhZBsM/v2/dM/f3dsbPyRPYcLufymnuyTr7vsKdddtr0frViSkA/DsWjlM3kmR9e620UvAUaZ0zSvQzCSjOrgwSDInWajPQHCbRxdZ4yzd9V9ew6WyvUrrziHPMkPiHzC1Vfe8uDgLbfd9aKn7mjTud+P7jkQq1fP2brZOhBSqCanWxCKi1SnhHN3bHci6b3D+XGeRhzsm9+/9a4f3PzzdDT0nF9+wpMu30hFmFQWZn55HGs/U+nKYbF5r+RLFMLWsVvgda0+xbwhEBHUNI0WvARrDW7xA6JotJdffjmKMlOrgGRpQAwejI7u2LFDjNGJBJvFcCWcTIg/ODi4efNmTB+gL4FkZZUwD1cgf+vWratfs6CEgAMBB5bFAT5tRLGiiv7FW6lXorJEWCCVCT5d3TKDd3RqemiCA/JKW9tCTI1GTpNSMZG/wIQJ7iaIFDw+EWku0kb1XBQ6hoA51hZ5JQQapYLI3EmRptrJzbKcP0vZRXSmRVFiLVSptaUjmVSaVT4PP/TA0MH9V11yydOedP0vXbmVc49rxWqyUooC1dVytR6KVSop9nvkkMRoJUSGwJhUHTv2gg5RSdVQcny2oARLfEujVZ6uOEdGppLJ1I5uToJy2hJOvlC55rKtHV9qf2zffkaHQx1O2nEOHx4ht6193UCIy2J5d2B7ORISAKaMTb2ZUDTzyOGpx6adHVnnjn0jn/3qV48UZ177yl97/lWbyAT0pXcVZpA/VE5EYzrpbkHiT8sHJ9I6V4QRICWIKA2db8y2CFc/4cCqFcEEK4DzO9/5DjAMxPLolltuecYznsEg8dOf/nQsz3fccQfhRP7ud7/LK9+1axcmawaPyZBA4ls+9vE0h1h4cA04EHCgtRxwocwlwhVHyOSoHGonwFApyJLTFGfldrRFI7Hv/fS+aCK7rb+7XhLwY1/iijePCHlOQg9+gDTQc9nyTeWRSCTwA8e0TY4Epg/Q3dlRLIvGaT0EtEieUt6KOUrM5VOJCPU9d8dZ9XLx2isv+5M/+N2P/P6vveCKgd5aOV0vZuMV0BfbcSWSrkZTkWQbkIvWHAqzTfOEU2NW1BhLiOclyfQcRCu2QMbyolEOSGCaqgwAj0/knWRKV1TJwqfb7tgbTSQvvXg3LwCrMEzuTEbLU/VfuuZK8PLL//VTOPDYuFOoMXEsNtAjM8cI4YVJuaFahLfFiqcwa6ccDins7Nk0XU0ycvC128r//JVv5MuVl//Kk1/xxIv7yNZxsswzN403jIW/FpM5Xsb4eStxGga2UgOmIwZGou/SJtB3f/KTnwDJuH379jH6i4J75ZVX/vEf//FHP/rR888/H5vzO9/5TqzNL37xi0FZbMvPe97zWJX0uc99bu/evW9/+9tf+9rXnnPOOWjV5EbO1uBOwzcWVCngwOnLAUVQRDCdcRfcIqwTlfFKZ2Bj34HxqXsf3JuvOv3MvtU9sNgXgrm9TOyVc+IVHEl8glrpbK6SH914XXyEfslCYOvV+5F4LujuluoHL+oBo+ynynNzVMkqn59Jpro7086bf/PFnKnUn3GyVheZIWVbTBv+S/2MOzxgn2qZvMU96q+A4GIi3VQRpK6ipRCAh22uxXocTbJ+l6z3HjxcKld37TiLjABRKGOzjO62xAVnb8Mcv/fwkRHHGWPPjkS2pzOV1rcg65ogjbzYq1O6VPIyyIocBnoHfvHgnd+88eitP/h2vDDyrOuvfP3zn57Svb14cfDVVlIrist7F5rOJCcvvlUOjARubc7zl770JZYbXXXVVSDom9/8Zvbc+Ju/+Rtw9F3vehcLkF75yldedNFFDP2y7ogkTI1muPcTn/gE0cDmt73tbRir3/e+91ERVGrGiQP0bdU7DcoNOLAsDsjIpOsQ4b44AvKQxaIMiR1aI51/9nZuHz4wWI8mtm/egEnWh5qyrqBV8a8inKQmyf283SKW8UeNaBwJzBEEVazfHLqnYO+ZW5eR06yo1Iaf2rK5CqB6sBPOZLMUxNONWWcgI+DHKO8k63YiiWIkVQ1nnLAAIgZoJkdLJrogeJqTi0KxqWhmJtZdiHZXRPOcxxk423iczY+hQwF7yAe1WzfpSORKstXW/Q8/wiSvy3Zv4pHyT7YSJNOLzk30drUdGBy+8+HqwdFSLZLs7+1jgS/vjO6SGATwquFA+wuSFuX20nPPvXT3BbfcdFO+XLrul659zaueHXfKCUiuTTvVPNGt8+Amn4fw0zzIb8NrXU/AlXag0+Lr6KwvfOELr7vuOgw+oCwQa71NLM+4z3zmM6YrW+vB5tzT00M0MPjf1Y2NjeHH8gx4Y2OxPOf0V9e6ekF5AQcCDiyVA8hqF33tBCHO9HWTFossQETE8+OM93K5WAm1JbOxTRu6E06xzAbJssESII2oZ2mMKF7ikOtybAKJTsTJUJjhFeCUStXrE5SRTiQNgC1HxIvEUoBaehkQRAqZnuRVEK9iD2ZlJiIlyZWSiSOGZpRSxp81AgRIInFUjceNcW7u+Flt7Socmc8RzUb3EJ4u4ZRCbcMhTNmQUY+HByedwcGhru7ubT0O5ythoy5XynH6BViMw87jLjjvmz+79fYHHk1GkoWK09PdbSyGToomEyvaagRZLD3qTYSGH7o705ndde7mt/z61W2QWppxYrLxlqnLOtBv5J7g+5qvrqdMWMvqjKqKnopJhBVEmETATtCXQJoIUAr/AFpav81wlsO55LMQh7EaPyhLKm6ZMQj64gG2cSxhIs8AfYVTgQs4sL45gOxXxG3ovoYlUM2pCFzZyg45jpbAb2NfLFSrR5JpJ5LoZqpSuMKOTAxkSmSAQh1SnZ/rZg8ve6HH+atA4uIvWaczSVkjG6olk+4s6OOkP95jIErBT7sIIdmBglJEkMmYt9SnnC+zkpLdLVhg2+bU2utOe72WrFWibJrMltecZCBO8uA/aYiMpRoJ2FGrtdUrHNugEea5UFYzAGvvgbCKclkOAIaM+x7cU6mUzj5rU7vjcMYCRcge1QBsuYCy+6RrrqiGwrfd+9D9jx1kU43NGwfoTMB6qmA/K9X112ucMnHNOd2XbEhdu7P7fW95RonhfPo0bK4pf9NOKKUFyEUKkp6JMGEe0k/fICrcGgdMgqPYn4FSENc+IbONgLvQZCFcMSnbel+wlgiM/qIWA8PkQHJ7xP6UtniJcNKSJJulWQYu4EDAgXXOAQGh2UqbiWDgoC57Cqt05m9n3Oloz47kw9VaqCPDpKFqNMysYYXHWl3mAGlkrS1ZggIn68hCNWA534fNIiR7GRg29fpEMicHUX/lB4Xi8FJPqb6sH0InibAMiUB5VJhxUE1jItAklg6vSgKjw0MqQXQeyUxtnUQcASgbjNC07oUifQAmSACYWVXsB1ILVyLRotr5b7vrLuyRl+4+F5MCO1BzVkQ4FldbcSVUci7eGevu6XvgwNHe9ko2lt5+1jZeFV0gYbeWaXDi1ghSa845Pc7vveZ555/bxfN6kh0apuuxdL4QYusQTSbEwhOSc9QFVHkdlGbCT2d/K7sbhr5wl1aOwwPcosXimZiYsLlUaMZAKTYT0BqsBX2b3wbgbbcsPQKVfdwN0LeZS4E/4MC65YCBjZGHFHZxScSyPtF9dfACMShNrDOsh6KJVLqnCy0KVa8e0X56s8VLkjVnuvyae7Y2SZmKJ0K1GpZhFUuog6ygPbnchTzw0s1EoEt/5VLFNrVO6uorLMJRBGE0Vg1Hi+FohdFfzhEKx8UoLOOsnNQgw8Au0jJ2zilMxAktiL4US7LmMWCpnlAiM6lhJebjvOM88ODDqWR817nbNCPZSsEtIhrhgCMCH3f5FbVwbDpXjCeS/f1pOgteVVyuUx2hT3In/0pksnzV2V0dTr7dmUxWBjtjkalyNZTKsCNHFfAhd6Iq9vMX5HYTWvIz4NoyADYDMhz2zcssK/INy2zNgQmaj4sQBoBBZb4x1F/8YDNqLsnxkxwPQG4QDu6SG4FmxD4DXl9QxYADpzYHXPlOJdSHPAJX+CnOoRzJYCwinnMFAeBzejID0amtGdlIEXRiUyiT4XIAvE7gslv1NzJeFoOkdAVHxQVmQdcT9Rl+KKigFAbYqgxaMjYrg8DEPMFiyMQjmOzIJEZXA3jX7YM0ZyzDVQZgwST/p4xhdzBXaNNdcX1SQzCYgxShCJ0kaiAt+KqOHDjGkSujudF6pVqxedXCZYHNkFMoy9+xvHN4aDQVDm/VTU5qWJklolPOo4vrxpyO80uXbt/UFkvX8plwBWujKL7wQbfmUGK4yEiw+HkUqkfTkXpu0ikB7rLSl2s8GjPeylRs+zVBdlONjPbT/Nqy+mJARseFuyiv+PHYcK/5uTVjMhiMsx4u3Tf8YDNPiYbfPL4ezC25cbWc8QQu4EDAgXXLAcQv37B8xiqIuSAIFEBYFoqwBlTYOKICsmTQ/fLFFzx+5/bxO3/1/GwlX3TCKey19VpV45EFSUWOKDaT0M11WXUX5GMdTVlQR4GJVTQpZ/KhtvAoM7ByKN3obZIjNGKgFQuxxFuWkzpzLBEKKxtPCtEQLHY8wmNxPYlY1uaKTIuyHUeGp/Q8iCD1kYt/5DB+mYrFT5z7R1DWRV3OTGJNMKbpkHQdJhTI21meW+JwXra8kCnWbDotcF7ISzGOc8/Dg6FY4rJduzp1UlU43haXDT6KsSwTsKX8rONcP+DsThcyI/deuDEJ8SV209SVSCjH/KgDdcIjNSIJg8esCM5knXibpm6LOulkSGpEgbK3Jz8ylpcFOySVWw/J6oxwwqjABRwIOBBwoCUcmCNwuUWs45hdJX1x2VpYYIbAbk4E7M58+N1v5mQkG6gCm6Pad2egykKachPAODFXKxXLkTSbIrIrRCxc68+E2lICswAW8GaYKwUtF3p9aiSxK3itat4T+g3i1Qt/JM7sCAQsXC+12kOkG0PUX4jFCbpanuFqOcrALps/Ekq8mgJwJEE8Qu+8/wHQc+tGNhlzJ5RrlhxhxCuQXJm8zNLdy7f3dxbHHnfuZvbJgsZioZKOu/umWL8HuuGNlo3yRNGkdYkiE3u/eMS5VXW9sx7p89P+EgDwaf+KgwoGHDj1OMDQkhnDsH4x2GRmMKxfZgCzASzf9GVDmytVyWQmLftPKopwSmCaUbB8HhADQ0AIF0lADvutVKkrkQ+QC9lcxaFWupZm9roQszA/jpTAtj86PpnTNUNs7BUJp61KQPI99z3ALK2dO3cuUjPWhD3raU85eO7OS1kXzHGNMIoV0gGMGM+Xfw04t3yeBSkCDgQcWGUO+POqAAzgllsLMej1B6qY/2FPTQM+WaJAnmo1XyyGk2lGaRmQZY+os/r7mZoMAANgiEsBYB998awz56nlDY0TGOYgQtkoOuJkElG2FolEYwKczJWpOxlV9Euc7zvjjIyO93R07jxLF15bRiz8EkTHmmzsFyZs6Y13Z89lj+hSvh7hBCWZi0PwLOd2AmaFBTfzcCAA4HmYEgQFHAg40FoO2AwPowG4RfwbEvtaL49AXzM+NweeLNnRUDIqR+QBPIyM7t4+8Fuvfc3Axk4AzX4u1AC9rJ8SAG6CupMte2XSQzk/MfwyN00msDFl2uHsRuZBhdGJaxUWlWAflhqEnFzImSzK3lR33rcfW3Nvd097snksVpDUMNgqLucWRp0utq2kOxJmFhidkwjLSUOiBQewu+w3GADwslkWJAg4EHBgtTkAAGN5BnR9ZRcMtkDC8RMO7q4k9EqVauVqSZbcsvonGi9z+lAqOtDZKZNF1ckEaRnUNCXYx5t1hMFGE1cma7lEc1oDg9m6bKstFa+UijfffHNvT/flj79o+wbBzEhC5pfdfu9DMHPnjm0AraSkpg1AZYsxt45JPaOZCJVSNZPQddrSMSFUuyPKH7fc4M8SOLCOms4SqA2iBBwIOHBGcMAsnnb1KwwA48AJH5V5xJpDNqP145yMB/gIY5+NxDj9N8bs3WSUOUehUjGC2sfGkAoxzJTGsfkEf9V7MgWuVlohTKcWawEs9uVwB9Frd52785euvSoei/7XN7/16c9++Y5Hiocm5QQkZmA9tmcfjL3scZdw6JP0MyQLQ3PR9PERQCZUv1YuCQpXitinayWSciIVV3OCxp4/+Ht8DgTMOj6PghgBBwIOrD0HwFoA2CzP7AFgo7+2+h9iCMH+jIc1h7YZ7cpRyKHyBVbZltgTt84u0LLMWMDIhSXK4YaRT5lnLDi1np2QjpMzHvht3xB63Suf+Wsve0k2k77hhhv+z9//3xt++MB0zRnOOxy7zrjwhWdxJKC7ElrrBvrOwgjWDqVYDepU02nM0PUwOjBdEZ2L3syGWWmaHwT+2RwITNCz+RHcBRwIOLBuOGAaMPqur/KCykbdys58nlPjOPsvsgCXc/rQ/kCXBvSiAwK7LhzPSbUebuGO0GdOJkK73nI+H02lGPrtiDlPuWrrrl1v+s53f/adG77/X//1Xz+++eYdl+xiC8zNG3uA6460nMEccy3RZCD2Z9F9yYncXGUYv5ZDebYLpSjcgVs2BwIAXjbLggQBBwIOnN4cULURFdfgrAFjHro1MG698QHKPeiEeHdhMUSmUrLYl0dFnUU10O485+lXXnj+ri984RuP7d83cct4rF4+b8cWWTJcY8dKw1pu3JlmUk3rhchYL9UnZ93B2fTj2ejrgb48U05K6sDNy4Gg2zIvW4LAgAMBB844DoAWDdTyYUS0YP/XDCnrDlwgHojl5zrbNdNwkFONdBsNpkOjdaHg92Wcy3e3vfddL3/O057kFKc609FLzt9JOIuDPb2ZfbUYDhYNeDZOcEfddftP+Tv7oVd4WFTx9dtT8chs8d9AA27xCwiKDzgQcGDdcEBsrQobTaDi46yCjpLKU9UC5aYppj5r4QUCEejWc+CqK5GMVKZJsSdlPRZlS03Zo6qAeg8eR5xE1HndS666/Mrz86WZXWdvInmNc5Fgg1RWzldu9EPEZ4BqHLGK65UALa+p7gH0NjFjYW8AwAvzJngScCDgwJnGAczOAieKWwY0xgHz6xWs4bjiObOT1gefZGaY0MiZCw3sFIyMRjkriVnLHKIM7ArCShU5gGG42tkbuWRbR8npQP3lUVzXFskzxVvraOg5VWQugTYk7F2VWwL2Dag2VknUwB2PAwEAH49DwfOAAwEHzhAOKMDIIUOCIR4Gz8YTjaLYpTFmP1wPbEK/FeJRf/FBrVEoqIoVmgOAQ0yakpMuVFt2+rs5l90ZGhuLJKPZNIcluPWW57oTtLFE7Ml6MpJitGjGBsxy1QJsJpakajhfCXbZ1XgS+DwOBADscSL4G3Ag4EDAAbDFMEdwxZDDtmM01jRGQ9cjqjBDyiYng486NOvprw67X7F1M1s/C3TKRKqwUyqUCpV4PBWKJzZ3t7GSl10pWV1VmioSyEQsMNU44TUK04A5L0qcYbBdvQguv/zbwHNcDgQAfFwWBRECDgQcOJM4YDqj1bjZv/55IKA7q2Pg38he2ZyeiLbLMcMy/BsGYuNyciDKbSkUDpVLhWq53J5pd7KcOuhOn6b2/s+dbOVNuTLGNLPHL2v982n9UBgA8Pp5FwElAQcCDrSUA814AiHurRhZfXSZE6Wl5B5bOMCp1mV94tMsVZEdORRWZQjYc3jRYRVT0/FMCDjGeUo+CYAHDyEY9bb8vMdeHv7f2ZzxC/c9fsTA0+CAx95GSOALOBBwIODAmcqB2TDic2GBYP/5+vE0AO8YmhuPGuR6kby/frdDojRhtasTE9iI2chlXt98xc0b8QwODHh0Br/8oOoBBwIOBBwIONA6DgQA3DreByUHHAg4EHAg4MAZzIEAgM/glx9UPeBAwIGAAwEHWseBAIBbx/ug5IADAQcCDgQcOIM5EADwGfzyg6oHHAg4EHAg4EDrOBAAcOt4H5QccCDgQMCBgANnMAdOWwDm+G47QxSPf4aoHS/a1tbGUd52sred6c2h34u0genpaXtq0Qoc0y3burlJ/BPCmwMXya0lj/wqVCqVlhBwwoUae3O5nLGdDX3Iah3Wwkgy8iAVgqGzuW2cMAfWMqHPWL/BrGXpSynL/+6I7LN33g9zfVahXC6bzIH+fD7PdWZmZikVX1dxWtJOEOMIc961yXNoiEajJtKtJcBbv0msK3YtQsxpC8A+6PLCcLCAl4cjnNtUKmURkskkjyYnJxfiEUmysjWMMzo6am/XkjS/acufOL5nodzWPhww4IO3KuA3Cg3M1p6YEyiR1wSYpdNpY3sikeBl8eGdQFarmgSSIAzyKAVSIRiy/Ua4qkWvSObWJGge5qHB0GzWYTvxPzHfQ/XnfJh8qgRSBT7eFWHOCmYSY/+peBzopa+GFJqYmMhkMs3CZAXLWo2sWthOeOPmmutFiH1loC+85TOk3eKa46xnf4gKrGf6Tp42+wjRhnkrdDY3bNhw4403nnfeeUhJWj8fA0KT12a6y7HFkaS7u5tMSI5UHRsbw9PZ2cn7JhX9L/t4fG3bemTH5tOqEGqHKgAY8J1DP9Whpn19fdb7bhVVSy8XOjs6OqAfxoJwXV1d7e3tR48eNTxeej6rHRPBRNOCQloIFPJZwW0aGEJ2tYtekfyhc2hoiNZCO4HbtBPoB8MW+i5WpNATyATG+t8ayRG+hKAMIXzHx8f5nGkh0I+HaCAx1TmBUlYvCUTCagQIHJ6amtqyZQseRArCZPUKXcGcW9VOTNLyrvHwZu2l8977+/sRxYTAQ146gpq3j41zBau8ullRk9Pb8Upw1JE3hHyEmzR6n6e8M/yLaCqGshbfvvze3l4/uQ+3eHy//3Q9eOzDNpoRUuuBpGXRYL1ag1uQeFlpWxLZiIRgOH+KMhy+WYNZn6jQ/K01f3RGs32kVIFH65B+6xBAmy9zTAS1pK2eTKHWtlvSTppfOmT8/Oc/B3R9FKNb4/vXv+f014D9RsbLoMe0Z88evgF6oHTweW3or/RAzULrx2z2oNnwjkkCfpOcz/u+++676qqrBgcH+XL4wsmBnPmcaBb4mxtHcz6t8kN/T0/PQw89xBV3+PBh6g7Bpri3iqqll4uyu3PnTt4a3Sb86De8Avq8VGHpmaxBTN477YSuHhowqvCBAwe2b9/+6KOP4l+D0k++CGvA6GcDAwMj6s4991z+rjdLg31r1oDhOWTzVfJtogndcsstu3fv5hXwVfJt8l3TTtYb/egAdNEgjNZCe96xY8e+ffs2b958qlikWthOeOk4XjrO/FzhpylUmGp49Uj1k/8W1jKH0xaA7VXNy0reEx8A3y2NyV4YL2+hfigvm28D0xwZkgQPaUmFnDUthyIMd61ZzFtiCwPpZFinm1rgwSgHABN4qphGka3wGYIRWKbQDA8P0+/mdbSQq8cWzds3wnhEO7FGQu9noXZ1bA6tDaF50KoBYAZcgC5rHnjWoUTz5S/fr3EbPpvlmUbCt0w7IQ4earEO2wk0Mypk5n3aNuMUNmbR2gawxNLXWzvhQ0NE8N5NRPPeqQjiYh2223k5fGoMPMxL+uKBfHg4+0SbY9JjormbnYrvlvfHy8PZm2uOaX4ywWP5kAr7Bk3Q1Ec/CR7z2/XYTFoYYuiLYOWbp+sNhXzwp4Qt15hm4okvCubTdeB9gb7w/9g320ImUzQkQZg1J1hNJ49mBvGnigaMwIJamgdtA8UXeIDb1nhay9h5S2/+0MwP/+kTg7jN4398vM0x581qjQNN/pjJ7eDBgxilaNs0lfVG50JsWW/thA6u38ely4VYoPt1qqAvTD5tZ0FbAzL45Mr3aX7aOu8JECUC3U/CF2pqFo42Y9oA3wkhfN728ZCbZUhgs3/x3Nb+KRWkCghTBCsKDaSeQugLu3hHyCz0G6pAXegqAW/rDX2hE5IgDPIgElIhGLIhfu3f+AmXSMOgedBI8FAFms1xv44TLuskEzZ/cb6fD9PQ1z5VPluqcJIFrXhy5A95mvxhLiR9nY0bN9JdW/GCVi/DlrcT/41TRxsZxMNL53MDjFGrVq/uK57zaQvACESc9Su5+n44iLHCMNX8XHm6EGcRqag1OHpVILcZ5d7znvfwqdPVoimQkO+HIpC/C2XSwnA+deg0g/kf/dEf2QAqrbaFJC1UNOzlEaw2rho/eVPve9/7TJLymgi0DhA8Bx4sJle7XSjnVQqnMRjN8BMHYYCuNTlMDu9973uRtj6GIRcMGCCmhQJ33qItEAqRrX/4h39odPrN27hHZS0aNV0lfi4xW1gKk42xEAOdiF3ItuS0H26NyHkru8RSVimakYRUwYMM+f/+v/8PbptBjhIJ9BsMt9RllchYYrY0b0jyqbLWTqeBhv2BD3yARk4+EGzO8qQ6xF9i/icWjbePs7S8axoAfl/r9T0nlvkapzptx4BXio9IfD4PYIBWZd8JYAAS0AoxOVo7wC7NF0X/C7TzoX2lCDjJfAAnPhtTxfg2IHi9UehXEMbat0QIYMCtYS23yFM4bNzma/drxCMCDYbNz3W1HWIRAnzauIU2CmV8nQFUiDE6CYHhiAPkFFckBSEQT3zitFA5hpmwF3pw0AMxsJ0Qaxg0Zl+PhGxqSnwLMcaCxK01TdPLgWCTszQSuM2HCZFUx/9I4bw1GL9FGfHr4cpESGa6NXclod9vNlYFXgf1QlVoFcG0UijxWyntBHpwMB/0tSaEBzGIFR0irWH7BDOcwauxR62qwilRbkN4nRLktoRIk7C+3GHuoj+wh7RCGIHHxKEJ+u2vJXQuVKjRZmLUvnO/LgslWftwQwKIRBghNM0mgfTnM4bD9GyQBdwiApqZTHVwFnltaDZMtbJM6DPhGXlKCJQjsyAYCiGYEDCAugDAWHS5tUFWxBb+VjkaA3T6HPMFqzVyq92cfiRgAPPtBRlU4MfTkipQtE88zRhWc0ughdMYrDPUEtqWUqi1GWMp1MJwmjeoRn+OKtBaiEATwkN/wgfppeS84nH43GCv33ohla+PXhrEQxtEcqVQrGsMuOAhxFqX//2uOEmnX4YBAC/pndonQfOiUdL15paPn+ZogtU+KmuvS8puDSMh9LErNhfoawnNgS33N1NFt5pP2pezhgomYaETbiMUrA8k8NsEwH6c1a4O7xoEQtD4BVnRx/ZsrFVQOyplNM+BQD+HtfRAFWQgXg2ufIiFTguh2ZiK6eMZcWj2pvfPQei1pJyyoMT6EHM6Af4HSC2IRvvhC11j2pZYnLUWa/N+o7X+hDUqP3CJGa54NJqH3xIQID490Mz3aBaR5mbgx4cSIpAWx9YujHOvOG2nU4YLjn2eTpU8mbrQsEjOt82VJoUA4sOmFYIQyC+6roTTUeXKLdiAZ105Q18INtq4+sC2ruhEmMJM4yQ0Q6TxFoINCQAwI5i3AJJRI25NwoLBa1kXBCU0mKDEnEjRNA9oAJ9MmzFioBADCa3CSIVmohHCtYWvAKEJS6GKlkwtjFRfwbUQEI5XgB+HMPXjEI06cktyC1z7K/RABoTZJ2nkwVUfAAiHSJoTbQPjxNpTuJQSaUJEg0hIpTHAcN4LjYcQqxeBNHjCl5LbasSBw2RLU4EqPEYVREIh6EuT5vO0MQuwmVuLT8+YyHyw9nUE6As3FneBBrw4f2SrdGuCR44cYb4i9ha+GRqWLfq0YT+yoGnyzbRQMC1UDQjjY7DPw9eGW2vaWohUC0foIGHnxPGX2No6ZljNe+H7x2Poa0js99PnJF/BW4qzspD4vnhq5ifCCCFFib5pzg/xyTg2xH+0xh64TXvGlkgLoV7cQps1eFoL4SZkaUXmITIcsOa0xqT6xcF5OMx1HX5uPpGLeKyFWwO2q8EV4TCWqvkyZ5FM1vIRTQLarFXQ5mkD1icmkA+QRkIgNKOTWDeUQACbOM3fxVoSfAqVFWjAx3lZNDsTtWZt/uhHP/rMZz6TNvf0pz/9m9/8JpNuaIVkgXgi8Dh5teIxhDFBdOvWrZDKPkF//Md/TC/VDImtIGfBMk2V+cY3vvFLv/RL7MEEGHzmM58hNt8wHGa+2x133PGCF7zgrLPO4ikVARKQC0QgpmHwglmv6ANTBSiack0kMSX+aU97GjIUAfTc5z4XOmkwlMktxONBNr3kJS8h/re//W1Ihf+G0CtK11IzA1aJisT8P//n/1x44YVUAWY+61nP+trXvkY4quSf/dmfXXHFFVB76aWX0lr27t1LOGTbC6KaiNelFrY68aybBfrCeewKL3/5y60cOmfveMc7GGKnUr/+679+6623rk75J5UrjflDH/oQwuT9738/nESbfOyxx17xilfQp+dT/ZVf+ZVPfvKT0E+bofH4Vp+TKvKEEts3xZXZzta2aRL0jC+66CKQla8AGKapIwwhm/28XvWqV7HvG3HoQ5AKR7HrUM6cEDNWMVGLv6VVrNkKZY2opUkBrrTCz372s3/+53/+5je/+Re/+MVTnvIU8IAJWTRKmiMNjsZnzW6FSl6ZbN71rnf927/929///d+zY+pf//Vf/9Vf/dXnP/95Q5GVKWCFcuGrhnuInpe97GV/8Rd/Qa4wnKt9w/fcc8/zn/98Nne86aabvvSlL33wgx8E5Hxu+x5ewQqRs2A2pnWBAZCHMAXJ7rrrrv/23/7b/fffD74SjiRCdOKMeFj96U9/GkkKbfjxIHyt07ZgGav5wIYkII8N/D784Q/v37+fVn311Ve/+MUvvvvuu8Ew2gmNnP1WQYJ//dd/Zekagbwd5Cx0wfbWAjD9AF4B9P/0pz/93Oc+R/OwkXVo+4M/+APaNo49KXkdrF6zTsNqsnPZed97771wFbLphMFYejz0cuA2XU/axuMe97hXv/rVbBzLh4DMaeEsYvuUuGLIoaNGPwwPXP3Zz35GM+YV/OQnP3nOc54DACMM//M//5M+BFvG8jmYyQ2pyFdpPdFl8+iMSgCbArc4B2hSREC2PvWpT33Na15jkZGhaGMf+chHUCkI4VviaqhgEdbJFTzDGTHQDP2IWnoV64Q8nww61HCYW7vyDSJe/UA+79e97nUMO/nx8SC8rCJEQygQsjb8t/bgUwJX8UMDV2adILOQTfgtGqgG1KEcIFKBOsKpoFGLv1UOAKPoQ4cO+QQg6//v//2/3MJScAsPwhQMBgbwW3xqxNOWE28MxKjz9a9/HZPDb/3Wb/HeaQz0DOicQS1+OhbcAtLctsRBEgy0oo17XGkkCA2Drte+9rU8RXrQJfqP//gPi0kSZtT/7d/+Lclb+5H6xGM/e/zjHw951s7x0ABwmAB///d/36pgxMN2PHbFM+czsTjBdQ4HAg34+N0tG5Lk9IUbbriBz8aMeCTDcEd3G+WA7wq1hkZpStvxc1zDGOeccw4fPB1YvudHHnnkO9/5zgtf+MLWKjHz1h6SgC4g1kQPt0ACV1NiUOKpCMoBegPi4Itf/CICAjUCzpObj9lrw3/aA8KIcrETckUb4NYUGuZkoSnaU6Ihc1/0ohf93d/9HTY6IptWwdPWjlbQEugNmKxHHKB4wV6uT3jCE6gOIVTBMAN+4ueNEJ9HvA4cxPufAIFr7PjKoAczw2//9m9j+eeUDgAMOlHceQW7du2CHhoGXyVG3RZaoWEUljPYCD323uHhS1/6UmzjjKHYUmAgCoP5+eef/0//9E/04Ok08HkyQoFaSY3IgcA1Zq9fHMQDpdzCcBRfjotAO2ckBVuUfafYe/hCsZ1cf/31l112GUMYsJ3IXEnIOzKx6WcYeOblQADA87KlEUhLQr5zpdkhjOifMmyDDEXs8oXziE+FK0/5/onWSLk+fH/6p3/6hje8AQCATsb20ICRAuuQThNScBj5jh+WIqQAA7iKnR9e/uVf/uWTnvQkrHOowsjfH/7whwSCJVyN+WvJb6iiOFhqhUIGQodR9v/5P/8nIonhCYOo//E//geohpYG2tFguBIf0Wb9hrUk2C+Lok2sw+cf/ehHyFB0sre//e1MaAAY6ABZ1ZCevAJAjirQ2oGK5jZjeOznuZYe2EivlyZBM6YKmD1hNbQxksojwMzo5F2AGQSuJW3NZQGf3EIhGExvAD96LbOyGWJnQsOmTZt4C7R2eHvzzTcDuvQvzz77bD5PtGGGWojPu6AWeFriIMze8uWXX85sDCzkDEzQS6MDQWu//fbbaSEf//jHuaX3xof5G7/xG9SONgO1SEgaOWDcEspPsULnaMTB7bwcoGHR+Hi1tEWLwHf1lre85dprr6Wd+UmQbr5/nXggmO+ZLvaDDz74sY99jI//K1/5yjqhrZkM2OhbmPnC6Ud/6lOfsgjsWQ/nn/3sZ3ML2xENqAhmwTPm83Z4xHUN+E8RiPhmypkeb7e0B/DggQceAGgJQZIiVVF8cdwikkAO8xvBzZmssR++URH4jAb5gx/84N3vfje9SZRIyCCQK3WEeMTrHMJg/howeU6hzbcMl3LyIABggXR36CXgZ2AVKxRNyJhPfwjN7L//9//enHYt/TRUK84aDGMQtA0GUGEvtrTrrrvune98JxGgEyI53pSvkkoxjE03gn4Dj1reSKDB5yd+HP0ePsy/+Zu/ufPOO/kkmfJGoNUUwUIr4tb4j4eOEdfALc4BsTgFbnEOIHSIYDrNt771LfwW8ju/8ztPfvKTLS0ttRmJF89wLZ/SjWXmtl8ie8+i6Bj9fuA68RgD+cjxoId94Qtf8AcdQS+mj/FtWwhqPYZoHwh9SPBDVrVGVgqU+KVAGDCAvoKxzgJpD8gjVAE0IdMkuOIHFZC5fsK195hYNCnZ3GJpyUwlgx6Op2W6DQjxjGc8A9nKEIDPXp/ateGzX1yzB1XMuIpaieUDPDDbiVlEaNhUzaCLRYNM1mtOu/Z+GjOFwsavfvWrIBbUgq9QblPhCMHszJWxIaMZxjJjA5MV3f21p7a5RNoAJPkvGnpgLA5D9P/6X/+Ldk5F/vmf/9ki0KQxojBN0mpBJ8PvfzTnGfiP5YDYSQK3OAdodkRAgDLmREeVtkXj41NHrcQOxiMaHEIBD/xdPKu1f8ocUQQo5QIJXDEcYRaD/rWn5LglQhikoorhgaVgMNSCc9wytsfgEzBMCDCGvZTlEGYshedEtsx9m/BxyzqZCJSC3IESqDVZg+Jy4403ggGYQMmZVcu0ByYHfe9732MYEmmFconuyywnehVIYXsjJ0PDCadl0JGmS3KQ1RiO0OQWZpqNh8lNNk0PUomAmZRHtHmErF/o2vDZL67Zwyo1hhthNQxnGQwo+9a3vhWFjPZANBoGjQRIZu4blolrrrmmOe1a+mm3FIeFnyuig1EJdHcmDDMvDEcjZzEFmi6MJQJtw7oRMJZJ9XyeZnxu5vlaEk9ZmBNoGNBjAxZ0GmAst8wmoUlgLee7s0EiItOkse4w2mJtCYFJnDUm+BQtzh3EOkWpXwOy0RIMXBFDrDjEcITVC5PRv/zLv/DxsG6P1oajCSKtDDbWgKqlF4EMQndknO+CCy748pe/TKcV+y0EQ+3SM1mDmGASjl4CX7h1cbDlIrCAXsQQC5MwQTMc9cQnPvHHP/4xIoyzhkxmwXNEwxpQaEVApA8/eCidBZ2s74K3tAH0BqiyngHjfBj/LT4ToYlM1wHBhOTyc1gzsv2C/C1NWF/E3BmEJlraP/7jP8JV5jwTDcsz9ANyCFOmSXNlAgEQYp1LqkPjQRz7Ga6xh4+RNuAXCmOBuosvvpgQejxve9vbQA5eCsMBdI4vueQSP+YaewyBeOO0XmgGU2kPeGgedHS2bdsGnfThYCwtHFsuUxwQNdioGXBhtQXU0k8yybPGlPvFWTPG2sRsUzjJR4dRh6csSeARA8CYAAmneTC2RSt63vOeR+14HVb3dShk/KqtIw/fVeAW5wBfgm+sYw4F3zxfDtsXMKrKp25pfVvN4lmt/VMEKDM7MDvT6Ua2vulNb4IGn+y1p2eREuEhKzh94W6HFvC1w39S8cGDZ9juWJVoq00I5L34nPc9ixSxUo/AIb9J8DHTA8D5dgVIhe1+WUgibmkzTFexQISy/3TtPbx9pCSD6OgxkM2GIbYRB5TAWGrhvwLrMdjYME+pMhXBrT3Nfok+64yrLIZBA+YpbwSHFZ0WggN9Gd72U629xxpts8EfGmCgTQL41V/9VWYtGVUo6yyoRZWnW0aXiM4Q4dSl5R8p/WAo4QMEYkFWrswJYDDbCMOCQqeBbgS9BEYrWKAPzbQrqxRx1kMVjJj1fA1B3DrqDgSkBBwIOBBwIOBAwIEzgwPBGPCZ8Z6DWgYcCDgQcCDgwDrjQADA6+yFBOQEHAg4EHAg4MCZwYEAgM+M9xzUMuBAwIGAAwEH1hkHAgBeZy8kICfgQMCBgAMBB84MDgQAfGa856CWAQcCDgQcCDiwzjgQAPA6eyEBOQEHAg4EHAg4cGZwIADgM+M9B7UMOBBwIOBAwIF1xoEAgNfZCwnIOTM48Mu//Mvs1mR1ZWsLPHbwIhs44G9enc/uWoSwgQNX27kQD842Jze/n4qE7FBhgWyGwDE75mcjBTa3Mj9X4rBngn9rybllnyyuJCSC0eBTwj4n9sgP8ZMHnoADAQdOjAMBAJ8Y34JUAQdOigOf/vSn2SKUrZHYjorTMkBf9p9ik0I2zCJf4NZwFJRlpyo2FWLPQsJtsy12g8LPfk8WEyi1VGzkCdCyySinIrKlM/sFsikgkS03DqMkvmEtQG77BXL0AoEktzw508Z6A2RCVmC/dQvImX2agF7y9AGehIELOBBw4GQ4EOwFfTLcC9IGHDhBDnBIhqmSaJagGo6M2Lsf7RMsZIc/boE9Q1nbOxrkw0MgUA1Msk8kG+Xbrv3AJJBJZDw4dgfk4GpyALANs/FbTLAW1Pf3+meTcB6B0CQhfzIhW+CZIiAM7OepXwp+yCNDFGujkJDABRwIOHDCHAg04BNmXZAw4MCJc4BNjDkPh3OHOJCOHYzBSzROsBMk5hQKTgPkliMX3/jGN3J8LMWAfBz4w274nEIBatpJvRwCwQ69oCang7znPe9BXQY42TSfQyfZ1p8cuOUIJhAU3P36179ukM95O5zrRUGA6Ktf/WpwF9AF0dmd+Nd+7dc4boSTa9k2/Pd///dBYtRr0BodHZWaaPg5kiRA3xN/8UHKgANNHAgAuIkZgTfgwFpxAMADUwE2EJRja2+77TbUSiCTw3A4GgF45ngZRm0J5zgaNFTwkghf/OIXgUDC//f//t+owiijmLI57vB973sf5yj8+Z//OeRzNBBHbnDlPD5yA2JJgtZrRmwOOOIYBk7mYPd8TvQDjDm3AHM04EoR//Ef/wFmc+buBz7wgU996lOccs8j4jBc/ZGPfISDqjifClM2ua0Vn4JyAg6czhwITNCn89sN6rZuOQCmcgAORmO0UsAY/RJdE7TjaGHOxuGsQNOGAdoXvOAF3ILN/f39KKa/+7u/6yugdjwcJuuXvOQlHFMDPFNfzoyyCMRn8NgmT1GQnd56ww03oGHffPPNHJ5IZHRlToAHlTksj1s06d/7vd/DHA3K/vu//zsg/frXvx4gR4F+4QtfCA1QhWZM12HdMjYgLODAKcSBAIBPoZcVkHr6cIDhVWzLIC4WYDASBRQNFZMvs6LQeoE64BlI5kxGDMs4YBUNmLNjAVdugU8U1s997nPM5LrnnntARGZXkQkMwo9mjCILxpMnmRMfdZmERODseuzbJCcaOVx77bVg87333gs9dAWIQ0J7tHnz5ttvvx0KQeXzzz8fNZoDXzk/GBM0CH36vImgJgEHWseBwATdOt4HJZ/BHABcbWoVSiooixKMAxdRN1FnUUl/8IMfoK1+//vfN/XUZkihKKODApmk4pherMd2FCuWYSzVwC3hOLIiGjCMHwQlvmEzgeAxY8ZEMN5jiwaeAVSiEYKmCyrjKI4QPAQCySDxZz7zGeaIoZE/6UlPwhZ9Br+6oOoBB1aMA4EGvGKsDDIKOLB0DoC+qLnEB+TQOEFKcBF0vPzyyzEmMycLZ+HgKLopkdGAwVEDRR4xzQq1mNFfYBWMZDIXKq9lC3aiEOMHRymCyDgyIRozuT7+8Y8ziEscrt/61rfwUBZ+Iljm2LSBZEgiQ5LHYjGeMiyN+5M/+RNgnjPkt23btvTKBjEDDgQcmJcDgQY8L1uCwIADq8sBrM0gK2Uw5Zh5yyAcc6mAOpbwYoJ++9vfzjAtNuqvfOUr73jHO4BPYhIfWLVUQCkLmfbs2fPlL3+ZhP/2b//2z//8z2i6REO3xnyNJo1abMt8DVZJCB5jQGZsGGPyfffdxxAvY8D4mVbNU/JEMwaG0YOhB8jHCs0t07v+4R/+4Y477mA2Fh70YOKvLneC3AMOnBkcCAD4zHjPQS3XGQcYczWz8F/+5V+iUGKFZt4yaLd79+4vfOELzJNC3QT/eLplyxZwETBGK8VGjX9oaIjasIQJbGZu1Cte8YpPfOITzFsGngknDkbspz71qTxizPhDH/oQSWxEGYBHf/385z9PcRdccMFzn/vcK6+88sMf/jCp2AML/RuoZvMNooHHpDrw/7dzB6kRw0AURMmtBub+N5mD5MEHk+1IWZYXRpK726ZgqLQt8vnQMB/T/Pv9fr1ecu0I86hSOiIQgUsCP37zlyVKj0AEviWgxWS4fY4lPOnbM+X3uFfTVgiV/Oxe9orYVNNMrrvRfrYiNcdrcHfVWR09rnWCfwI0xBy8XO+WvZ2m2913L5xdsrI32J5NTaXsyTJez73cPdIqbKVzBCJwTCABH6MrMQK3BDhvGjYgY+Voj/yo0bqDgC1ux9Y+AIsUwIu+3T6332dgl+bU7diaLJcu2GYrPtb72hFN5KoJEGnF2ZR9aXvGndSJ1oqaOyslhY/pX/xz9wYRiMAZgX5FZ9zKisA/EOA2PtOzsi/zqUiNZMl8Vhw2ZFk0cCZFSp4sxZAuL/p/HS75fOuswjpjZxXImy+tz6O+EHOtS87Tp2rGAtRkXwO38Dyzr6lFU8Ge0+0carJ49gWnIwL3BOqA7xlWIQJfE+A8fe22TS2Z3ubR/e9l7uQ56tXXch4X2gXtnbDgvW3+e0vVnnZ5fbAVuYqsh970aZENrLjEtXsFvcb3qTlzL1fH7POzZ3DVXfy5YPpENohABI4J1AEfoysxAucE2JF9iZDSVCE59qU9U//yguTIT4ypmLWntLcGV/trnbCXayBSvE5XKd2qlDWpFo0tCtjuqlWQYjcW6XK5sqayhC1YjESHDtsi9TrWoJuKn56NOyIQgRsCdcA39MqNQAQiEIEIHBKoAz4EV1oEIhCBCETghkACvqFXbgQiEIEIROCQQAI+BFdaBCIQgQhE4IZAAr6hV24EIhCBCETgkEACPgRXWgQiEIEIROCGQAK+oVduBCIQgQhE4JBAAj4EV1oEIhCBCETghsAvpY5m74ExI7QAAAAASUVORK5CYII=", "text/plain": [ - "" + "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.convergence_graph" + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(nrows=1, ncols=1)\n", + "axes.plot(cost_values)\n", + "axes.set_xlabel(\"Iterations\")\n", + "axes.set_ylabel(\"Cost\")\n", + "axes.set_title(\"Cost convergence\")" ] }, { "cell_type": "markdown", - "id": "615ed612-b835-4bf0-aa92-92d30ef8006d", + "id": "d96714fd-2d11-4d13-9d21-4fc111a95670", "metadata": { "tags": [] }, @@ -431,25 +330,17 @@ }, { "cell_type": "markdown", - "id": "670eddd3-2da7-4a88-b571-7884ef24f60c", + "id": "96ea8543-29cb-4570-8741-7199dea8a948", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm:" + "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `get_results` method:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "516d78ba-2951-46eb-b1af-efe877513556", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.023922Z", - "iopub.status.busy": "2024-05-07T16:03:59.023674Z", - "iopub.status.idle": "2024-05-07T16:03:59.177765Z", - "shell.execute_reply": "2024-05-07T16:03:59.177036Z" - }, - "tags": [] - }, + "execution_count": 11, + "id": "bbc7c2ca-1d3a-4342-9a37-5b8fc8980fbe", + "metadata": {}, "outputs": [ { "data": { @@ -472,85 +363,68 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", - " 525\n", - " 0.001\n", - " 0.0\n", - " [0, 1, 1, 1, 1, 0, 1, 0, 0, 0]\n", - " 1\n", + " 0\n", + " {'x_0': 0, 'x_1': 0, 'x_2': 1, 'x_3': 1, 'x_4'...\n", + " 0.019043\n", + " 1.0\n", " \n", " \n", - " 29\n", - " 0.004\n", - " 0.0\n", - " [1, 1, 1, 0, 0, 0, 0, 1, 1, 0]\n", - " 4\n", + " 281\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", + " 0.000977\n", + " 1.0\n", " \n", " \n", - " 511\n", - " 0.001\n", - " 0.0\n", - " [1, 1, 0, 0, 0, 1, 1, 0, 1, 0]\n", - " 1\n", + " 59\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", + " 0.004395\n", + " 1.0\n", " \n", " \n", - " 494\n", - " 0.001\n", - " 0.0\n", - " [0, 1, 0, 0, 0, 1, 1, 0, 0, 1]\n", - " 1\n", + " 282\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", + " 0.000977\n", + " 1.0\n", " \n", " \n", - " 424\n", - " 0.001\n", - " 0.0\n", - " [0, 0, 0, 1, 1, 1, 1, 0, 0, 1]\n", - " 1\n", + " 284\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", + " 0.000977\n", + " 1.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "525 0.001 0.0 [0, 1, 1, 1, 1, 0, 1, 0, 0, 0] 1\n", - "29 0.004 0.0 [1, 1, 1, 0, 0, 0, 0, 1, 1, 0] 4\n", - "511 0.001 0.0 [1, 1, 0, 0, 0, 1, 1, 0, 1, 0] 1\n", - "494 0.001 0.0 [0, 1, 0, 0, 0, 1, 1, 0, 0, 1] 1\n", - "424 0.001 0.0 [0, 0, 0, 1, 1, 1, 1, 0, 0, 1] 1" + " solution probability cost\n", + "0 {'x_0': 0, 'x_1': 0, 'x_2': 1, 'x_3': 1, 'x_4'... 0.019043 1.0\n", + "281 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.000977 1.0\n", + "59 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.004395 1.0\n", + "282 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.000977 1.0\n", + "284 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.000977 1.0" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " set_partition_model,\n", - " vqe_result=result,\n", - " penalty_energy=qaoa_config.penalty_energy,\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)" + "optimization_result = combi.get_results()\n", + "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "687f492b-a4a5-49c6-964c-8959b035bb93", + "id": "2a6d978a-f2a2-46a0-8deb-bdfa6c9c3405", "metadata": {}, "source": [ "And the histogram:" @@ -558,31 +432,15 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", + "execution_count": 12, + "id": "85fdf055-cb4e-4756-8983-3a607cc22382", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.182649Z", - "iopub.status.busy": "2024-05-07T16:03:59.181402Z", - "iopub.status.idle": "2024-05-07T16:03:59.666268Z", - "shell.execute_reply": "2024-05-07T16:03:59.665547Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/plain": [ - "array([[]], dtype=object)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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", "text/plain": [ "
" ] @@ -592,7 +450,12 @@ } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + ")\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" ] }, { @@ -600,20 +463,14 @@ "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0", "metadata": {}, "source": [ - "Let us plot the solution:" + "Let us plot the best solution:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.669215Z", - "iopub.status.busy": "2024-05-07T16:03:59.668565Z", - "iopub.status.idle": "2024-05-07T16:03:59.672400Z", - "shell.execute_reply": "2024-05-07T16:03:59.671836Z" - }, "tags": [] }, "outputs": [], @@ -623,15 +480,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "f349d9fb-132e-4ca0-8433-db1e2d61efa1", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.674917Z", - "iopub.status.busy": "2024-05-07T16:03:59.674535Z", - "iopub.status.idle": "2024-05-07T16:03:59.679208Z", - "shell.execute_reply": "2024-05-07T16:03:59.678640Z" - }, "tags": [] }, "outputs": [ @@ -639,15 +490,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "P1= [7, 1, 8, 8, 11] , total sum: 35\n", - "P2= [4, 8, 11, 5, 7] , total sum: 35\n", - "difference= 0\n" + "P1= [3, 10, 6, 7] , total sum: 26\n", + "P2= [5, 5, 9, 1, 4, 1] , total sum: 25\n", + "difference= 1\n" ] } ], "source": [ - "p1 = [mylist[i] for i in range(len(mylist)) if best_solution[i] == 0]\n", - "p2 = [mylist[i] for i in range(len(mylist)) if best_solution[i] == 1]\n", + "p1 = [mylist[i] for i in range(len(mylist)) if best_solution[f\"x_{i}\"] == 0]\n", + "p2 = [mylist[i] for i in range(len(mylist)) if best_solution[f\"x_{i}\"] == 1]\n", "print(\"P1=\", p1, \", total sum: \", sum(p1))\n", "print(\"P2=\", p2, \", total sum: \", sum(p2))\n", "print(\"difference= \", abs(sum(p1) - sum(p2)))" @@ -663,15 +514,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.682282Z", - "iopub.status.busy": "2024-05-07T16:03:59.681909Z", - "iopub.status.idle": "2024-05-07T16:03:59.740657Z", - "shell.execute_reply": "2024-05-07T16:03:59.739961Z" - }, "pycharm": { "name": "#%%\n" }, @@ -689,19 +534,19 @@ " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", " 0 : 0 : 1.0 : 1 : False : False : Binary\n", " 1 : 0 : 1.0 : 1 : False : False : Binary\n", - " 2 : 0 : 1.0 : 1 : False : False : Binary\n", - " 3 : 0 : 0.0 : 1 : False : False : Binary\n", - " 4 : 0 : 1.0 : 1 : False : False : Binary\n", + " 2 : 0 : 0.0 : 1 : False : False : Binary\n", + " 3 : 0 : 1.0 : 1 : False : False : Binary\n", + " 4 : 0 : 0.0 : 1 : False : False : Binary\n", " 5 : 0 : 0.0 : 1 : False : False : Binary\n", " 6 : 0 : 0.0 : 1 : False : False : Binary\n", " 7 : 0 : 0.0 : 1 : False : False : Binary\n", - " 8 : 0 : 0.0 : 1 : False : False : Binary\n", + " 8 : 0 : 1.0 : 1 : False : False : Binary\n", " 9 : 0 : 1.0 : 1 : False : False : Binary\n", "\n", " Objectives:\n", " cost : Size=1, Index=None, Active=True\n", " Key : Active : Value\n", - " None : True : 0.0\n", + " None : True : 1.0\n", "\n", " Constraints:\n", " None\n" @@ -719,15 +564,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "2e5c1b07-6060-455d-81ed-e48a2207c81b", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.743360Z", - "iopub.status.busy": "2024-05-07T16:03:59.742870Z", - "iopub.status.idle": "2024-05-07T16:03:59.746421Z", - "shell.execute_reply": "2024-05-07T16:03:59.745852Z" - }, "tags": [] }, "outputs": [], @@ -737,15 +576,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:03:59.748666Z", - "iopub.status.busy": "2024-05-07T16:03:59.748361Z", - "iopub.status.idle": "2024-05-07T16:03:59.752921Z", - "shell.execute_reply": "2024-05-07T16:03:59.752289Z" - }, "tags": [] }, "outputs": [ @@ -753,9 +586,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "P1= [7, 8, 8, 5, 7] , total sum: 35\n", - "P2= [4, 8, 11, 1, 11] , total sum: 35\n", - "difference= 0\n" + "P1= [5, 9, 6, 1, 4] , total sum: 25\n", + "P2= [3, 10, 5, 7, 1] , total sum: 26\n", + "difference= 1\n" ] } ], @@ -804,7 +637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/applications/optimization/set_partition/set_partition.qmod b/applications/optimization/set_partition/set_partition.qmod index 98a0674fb..2b37221c3 100644 --- a/applications/optimization/set_partition/set_partition.qmod +++ b/applications/optimization/set_partition/set_partition.qmod @@ -1,713 +1,25 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=324.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=18.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=24.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=24.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=24.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=24.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=24.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=30.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=30.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=30.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=30.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=30.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=36.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=36.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=36.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=36.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=36.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=40.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=48.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=54.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=54.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=54.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=54.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=54.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=60.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=66.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=66.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=66.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=66.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=66.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=72.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=88.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=90.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=108.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=110.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=132.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=198.0 - } -]; - -qfunc main(params_list: real[6], output target: qbit[10]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + x_0: qbit; + x_1: qbit; + x_2: qbit; + x_3: qbit; + x_4: qbit; + x_5: qbit; + x_6: qbit; + x_7: qbit; + x_8: qbit; + x_9: qbit; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=False, -initial_point=[0.0, 0.006952841596130592, 0.003476420798065296, 0.003476420798065296, 0.006952841596130592, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=60, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[6], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 3) { + phase (-((((((((((((6 * v.x_0) + (20 * v.x_1)) + (10 * v.x_2)) + (10 * v.x_3)) + (18 * v.x_4)) + (12 * v.x_5)) + (2 * v.x_6)) + (8 * v.x_7)) + (14 * v.x_8)) + (2 * v.x_9)) - 51) ** 2), params[i]); + apply_to_all(lambda(q) { + RX(params[3 + i], q); + }, v); + } +} diff --git a/applications/optimization/set_partition/set_partition.synthesis_options.json b/applications/optimization/set_partition/set_partition.synthesis_options.json index 0967ef424..df25d261f 100644 --- a/applications/optimization/set_partition/set_partition.synthesis_options.json +++ b/applications/optimization/set_partition/set_partition.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "cz", + "x", + "u2", + "ry", + "rz", + "s", + "r", + "z", + "u", + "t", + "u1", + "id", + "sxdg", + "sdg", + "h", + "sx", + "rx", + "tdg", + "y", + "p", + "cx", + "cy" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 2863520222 + } +} diff --git a/applications/physical_systems/ising_model/ising_model.ipynb b/applications/physical_systems/ising_model/ising_model.ipynb index 75dc17651..cdd184f07 100644 --- a/applications/physical_systems/ising_model/ising_model.ipynb +++ b/applications/physical_systems/ising_model/ising_model.ipynb @@ -29,12 +29,6 @@ "execution_count": 1, "id": "c6bb59fb-285b-439d-871e-7952c2df8db2", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:54.176458Z", - "iopub.status.busy": "2024-05-07T15:22:54.174237Z", - "iopub.status.idle": "2024-05-07T15:22:54.375957Z", - "shell.execute_reply": "2024-05-07T15:22:54.375173Z" - }, "tags": [] }, "outputs": [], @@ -64,12 +58,6 @@ "execution_count": 2, "id": "37e95f02-075e-45f6-94f1-6951ea81b0a4", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:54.381762Z", - "iopub.status.busy": "2024-05-07T15:22:54.380282Z", - "iopub.status.idle": "2024-05-07T15:22:54.389802Z", - "shell.execute_reply": "2024-05-07T15:22:54.389106Z" - }, "tags": [] }, "outputs": [], @@ -114,12 +102,6 @@ "execution_count": 3, "id": "c053de5b-bbb4-4c8e-b86a-840d792c407f", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:54.394509Z", - "iopub.status.busy": "2024-05-07T15:22:54.393412Z", - "iopub.status.idle": "2024-05-07T15:22:54.399755Z", - "shell.execute_reply": "2024-05-07T15:22:54.399079Z" - }, "tags": [] }, "outputs": [], @@ -136,124 +118,79 @@ "source": [ "## 3. Optimize Using to Quantum Optimization Algorithm\n", "\n", - "We will now create a QAOA model for the optimization problem. The results of the model is the sequance of qubit values giving the minimized energy for the protein. In order to optimize the results, we recommend the user to explore the number of repatitions for the model (`num_layers`) and the number of iterations for the optimizer (`max_iteration`)." + "We will now create a QAOA model for the optimization problem. The results of the model is the sequance of qubit values giving the minimized energy for the protein. In order to optimize the results, we recommend the user to explore the number of repatitions for the model (`num_layers`) and the number of iterations for the optimizer (`maxiter`)." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "0cad145c-b78a-4c9e-945a-fffb7e4fef45", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:54.404600Z", - "iopub.status.busy": "2024-05-07T15:22:54.403259Z", - "iopub.status.idle": "2024-05-07T15:22:57.272833Z", - "shell.execute_reply": "2024-05-07T15:22:57.262051Z" - }, - "tags": [] - }, + "execution_count": 5, + "id": "b2c6406f-8aa3-4674-870e-13b5a36b50dc", + "metadata": {}, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", - "\n", - "qaoa_config = QAOAConfig(num_layers=5)\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "optimizer_config = OptimizerConfig(\n", - " max_iteration=100,\n", - " alpha_cvar=0.7,\n", - ")\n", + "combi = CombinatorialProblem(pyo_model=ising_model, num_layers=5, penalty_factor=10)\n", "\n", - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=ising_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" + "qmod = combi.get_model()\n", + "write_qmod(qmod, \"ising_model\")" ] }, { "cell_type": "markdown", - "id": "ae3233b3-9805-4c8a-b115-d4fa9defcd92", + "id": "5f476b81-ce50-4cc5-a8d7-13c7b5033e0e", "metadata": {}, "source": [ - "We also set the quantum backend we want to execute on:" + "Now we can create a quantum circuit using the `get_qprog` command and show it" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "4f779a6a-dae4-4235-92be-f90820fbfbeb", + "execution_count": 9, + "id": "0b28f2dc-26ec-4975-9e1f-41adfdd75cb4", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:57.280659Z", - "iopub.status.busy": "2024-05-07T15:22:57.280120Z", - "iopub.status.idle": "2024-05-07T15:22:57.308215Z", - "shell.execute_reply": "2024-05-07T15:22:57.305665Z" + "pycharm": { + "name": "#%%\n" }, "tags": [] }, - "outputs": [], - "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", - "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2425da2e-35c9-49a7-8479-c1856ee34f75", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:57.312667Z", - "iopub.status.busy": "2024-05-07T15:22:57.312406Z", - "iopub.status.idle": "2024-05-07T15:22:57.344678Z", - "shell.execute_reply": "2024-05-07T15:22:57.343993Z" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/0b452fbf-fc23-4a09-b800-6ad828f47e96?version=0.61.0.dev7\n" + ] } - }, - "outputs": [], + ], "source": [ - "write_qmod(qmod, \"ising_model\")" + "qprog = combi.get_qprog()\n", + "show(qprog)" ] }, { "cell_type": "markdown", - "id": "5f476b81-ce50-4cc5-a8d7-13c7b5033e0e", + "id": "b2ae85bb-2625-47ea-a112-5bbbd37fd6d5", "metadata": {}, "source": [ - "Now we can create a quantum circuit using the `synthesize` command and show it" + "We also set the quantum backend we want to execute on:" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "0b28f2dc-26ec-4975-9e1f-41adfdd75cb4", + "execution_count": 15, + "id": "4f779a6a-dae4-4235-92be-f90820fbfbeb", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:22:57.347982Z", - "iopub.status.busy": "2024-05-07T15:22:57.347558Z", - "iopub.status.idle": "2024-05-07T15:23:01.963192Z", - "shell.execute_reply": "2024-05-07T15:23:01.962520Z" - }, - "pycharm": { - "name": "#%%\n" - }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/e1a07618-2899-4a95-9d21-8eeb100cb053?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], + "outputs": [], "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" + "from classiq.execution import *\n", + "\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", + ")" ] }, { @@ -261,25 +198,32 @@ "id": "8ce840ba-1d3e-461c-99f2-5045d8cb8e29", "metadata": {}, "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" + "We now solve the problem by calling the `optimize` function:" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "6daf89c6-2e5c-41b5-b162-dfd048014919", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:23:01.965793Z", - "iopub.status.busy": "2024-05-07T15:23:01.965322Z", - "iopub.status.idle": "2024-05-07T15:23:09.428158Z", - "shell.execute_reply": "2024-05-07T15:23:09.427541Z" - }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.15 s, sys: 211 ms, total: 8.36 s\n", + "Wall time: 2min 39s\n" + ] + } + ], "source": [ - "result = execute(qprog).result_value()" + "%%time\n", + "cost_values = []\n", + "optimized_params = combi.optimize(\n", + " execution_preferences, maxiter=100, cost_trace=cost_values, quantile=0.7\n", + ")" ] }, { @@ -292,33 +236,41 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "id": "370e71c6-764e-4296-882a-d5daa14d3176", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:23:09.430991Z", - "iopub.status.busy": "2024-05-07T15:23:09.430811Z", - "iopub.status.idle": "2024-05-07T15:23:09.458569Z", - "shell.execute_reply": "2024-05-07T15:23:09.457962Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ - "" + "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 9, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.convergence_graph" + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(nrows=1, ncols=1)\n", + "axes.plot(cost_values)\n", + "axes.set_xlabel(\"Iterations\")\n", + "axes.set_ylabel(\"Cost\")\n", + "axes.set_title(\"Cost convergence\")" ] }, { @@ -328,24 +280,16 @@ "source": [ "## 4. Present Quantum Results\n", "\n", - "We hereby present the optimization results. Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n", + "we call the `get_results` method to get samples with the optimzied parameters. We hereby present the optimization results. Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n", "\n", "We remind that in the notation of the solution \"0\" indicate \"-1\" spin value, and \"1\" indicates \"1\" spin value." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "da738964-b1ba-4b94-997a-ae7a46254f6c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:23:09.461338Z", - "iopub.status.busy": "2024-05-07T15:23:09.460808Z", - "iopub.status.idle": "2024-05-07T15:23:09.487645Z", - "shell.execute_reply": "2024-05-07T15:23:09.487039Z" - }, - "tags": [] - }, + "execution_count": 18, + "id": "2c35bcde-2d6e-4ed0-aa70-325dbf5a7846", + "metadata": {}, "outputs": [ { "data": { @@ -368,91 +312,99 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", " 0\n", - " 0.189\n", + " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", + " 0.608887\n", " -180.0\n", - " [0, 0, 0, 0, 0, 0]\n", - " 189\n", " \n", " \n", - " 7\n", - " 0.033\n", + " 21\n", + " {'z_0': 1, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", + " 0.007812\n", " -100.0\n", - " [0, 0, 1, 0, 0, 0]\n", - " 33\n", " \n", " \n", - " 8\n", - " 0.032\n", + " 19\n", + " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", + " 0.008789\n", " -100.0\n", - " [0, 1, 0, 0, 0, 0]\n", - " 32\n", " \n", " \n", - " 9\n", - " 0.030\n", + " 16\n", + " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 1, 'z_4'...\n", + " 0.010254\n", " -100.0\n", - " [0, 0, 0, 0, 0, 1]\n", - " 30\n", " \n", " \n", - " 10\n", - " 0.030\n", + " 14\n", + " {'z_0': 0, 'z_1': 0, 'z_2': 1, 'z_3': 0, 'z_4'...\n", + " 0.011230\n", " -100.0\n", - " [0, 0, 0, 0, 1, 0]\n", - " 30\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "0 0.189 -180.0 [0, 0, 0, 0, 0, 0] 189\n", - "7 0.033 -100.0 [0, 0, 1, 0, 0, 0] 33\n", - "8 0.032 -100.0 [0, 1, 0, 0, 0, 0] 32\n", - "9 0.030 -100.0 [0, 0, 0, 0, 0, 1] 30\n", - "10 0.030 -100.0 [0, 0, 0, 0, 1, 0] 30" + " solution probability cost\n", + "0 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.608887 -180.0\n", + "21 {'z_0': 1, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.007812 -100.0\n", + "19 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.008789 -100.0\n", + "16 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 1, 'z_4'... 0.010254 -100.0\n", + "14 {'z_0': 0, 'z_1': 0, 'z_2': 1, 'z_3': 0, 'z_4'... 0.011230 -100.0" ] }, - "execution_count": 10, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " ising_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)" + "optimization_result = combi.get_results()\n", + "optimization_result.sort_values(by=\"cost\").head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "06d70826-8ba1-4705-ae87-1a3c4ba5d69a", + "metadata": {}, + "source": [ + "Best Solution:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "1b2775b1-7b12-4a0a-9931-eacdee4e5127", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4': 0, 'z_5': 0}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimization_result.sort_values(by=\"cost\").iloc[0].solution" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "id": "81d6f1dd-7e65-4118-bc8b-9ee662c72a68", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:23:09.490132Z", - "iopub.status.busy": "2024-05-07T15:23:09.489947Z", - "iopub.status.idle": "2024-05-07T15:23:09.702616Z", - "shell.execute_reply": "2024-05-07T15:23:09.701922Z" - }, "tags": [] }, "outputs": [ @@ -462,13 +414,13 @@ "array([[]], dtype=object)" ] }, - "execution_count": 11, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -518,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/applications/physical_systems/ising_model/ising_model.qmod b/applications/physical_systems/ising_model/ising_model.qmod index aeea9aad6..a40985b4c 100644 --- a/applications/physical_systems/ising_model/ising_model.qmod +++ b/applications/physical_systems/ising_model/ising_model.qmod @@ -1,155 +1,21 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-20.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=-10.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-10.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=-10.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-10.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-10.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-10.0 - } -]; - -qfunc main(params_list: real[10], output target: qbit[6]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + z_0: qbit; + z_1: qbit; + z_2: qbit; + z_3: qbit; + z_4: qbit; + z_5: qbit; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=False, -initial_point=[0.0, 0.027272727272727275, 0.006818181818181819, 0.020454545454545454, 0.013636363636363637, 0.013636363636363637, 0.020454545454545454, 0.006818181818181819, 0.027272727272727275, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=100, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[10], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 5) { + phase (-(((((((((((((40.0 * v.z_0) + (40.0 * v.z_1)) + (40.0 * v.z_2)) + (40.0 * v.z_3)) + (40.0 * v.z_4)) + (40.0 * v.z_5)) + ((10 - (20 * v.z_0)) * ((2 * v.z_1) - 1))) + ((10 - (20 * v.z_0)) * ((2 * v.z_5) - 1))) + ((10 - (20 * v.z_1)) * ((2 * v.z_2) - 1))) + ((10 - (20 * v.z_2)) * ((2 * v.z_3) - 1))) + ((10 - (20 * v.z_3)) * ((2 * v.z_4) - 1))) + ((10 - (20 * v.z_4)) * ((2 * v.z_5) - 1))) - 120.0), params[i]); + apply_to_all(lambda(q) { + RX(params[5 + i], q); + }, v); + } +} diff --git a/applications/physical_systems/ising_model/ising_model.synthesis_options.json b/applications/physical_systems/ising_model/ising_model.synthesis_options.json index 0967ef424..cb806fc57 100644 --- a/applications/physical_systems/ising_model/ising_model.synthesis_options.json +++ b/applications/physical_systems/ising_model/ising_model.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "ry", + "tdg", + "id", + "t", + "cx", + "sx", + "h", + "u2", + "y", + "s", + "p", + "sdg", + "sxdg", + "x", + "cy", + "u1", + "rz", + "r", + "cz", + "z", + "rx", + "u" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 234206522 + } +} From 0206d63dbeb10d33e817883ef0620b89879daae8 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Sun, 8 Dec 2024 14:54:13 +0200 Subject: [PATCH 16/38] removed knapsack binary notebook --- .../knapsack_binary/knapsack_binary.ipynb | 642 ------------------ .../knapsack_binary.metadata.json | 7 - .../knapsack_binary/knapsack_binary.qmod | 265 -------- .../knapsack_binary.synthesis_options.json | 1 - 4 files changed, 915 deletions(-) delete mode 100644 applications/optimization/knapsack_binary/knapsack_binary.ipynb delete mode 100644 applications/optimization/knapsack_binary/knapsack_binary.metadata.json delete mode 100644 applications/optimization/knapsack_binary/knapsack_binary.qmod delete mode 100644 applications/optimization/knapsack_binary/knapsack_binary.synthesis_options.json diff --git a/applications/optimization/knapsack_binary/knapsack_binary.ipynb b/applications/optimization/knapsack_binary/knapsack_binary.ipynb deleted file mode 100644 index 077e929aa..000000000 --- a/applications/optimization/knapsack_binary/knapsack_binary.ipynb +++ /dev/null @@ -1,642 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "13d83b59-57f7-48ee-8bff-deda7d28edc5", - "metadata": { - "tags": [] - }, - "source": [ - "\n", - "# Binary Knapsack\n" - ] - }, - { - "cell_type": "markdown", - "id": "6d56bb6d-9f3b-45db-8e98-b50f27af7505", - "metadata": {}, - "source": [ - "## Background\n", - "\n", - "Given a set of items, determine how many items to put in the knapsack to maximize their summed value.\n", - "\n", - "#### Define:\n", - "\n", - "- $x_i$ is the number of items from each type.\n", - "\n", - "- $v_i$ is the value of each item.\n", - "\n", - "- $w_i$ is the weight of each item.\n", - "\n", - "- $D$ is the range of $x$.\n", - "\n", - "Find $x$ that maximizes the value: $\\begin{aligned}\n", - "\\max_{x_i \\in D} \\Sigma_i v_i x_i\\\\\n", - "\\end{aligned}$\n", - "\n", - "and constrained by the weight: $\\begin{aligned}\n", - "\\Sigma_i w_i x_i = C\n", - "\\end{aligned}$\n", - "\n", - "## Problem Versions\n", - "\n", - "**Binary Knapsack**\n", - "\n", - "Range: $D = \\{0, 1\\}$\n", - "\n", - "**Integer Knapsack**\n", - "\n", - "Range: $D = [0, b]$\n" - ] - }, - { - "cell_type": "markdown", - "id": "fdbcef63-3c8e-4f0a-a4fb-9058807fa3a8", - "metadata": { - "tags": [] - }, - "source": [ - "## Knapsack with binary variables and equality constraint\n" - ] - }, - { - "cell_type": "markdown", - "id": "0bdc4e6a-199b-44b3-bd3f-fd24722b616b", - "metadata": {}, - "source": [ - "### Define the optimization problem" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "83ddbd07-f7ab-4d80-b357-3890622d395f", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:29.057270Z", - "iopub.status.busy": "2024-05-07T16:07:29.055577Z", - "iopub.status.idle": "2024-05-07T16:07:29.609395Z", - "shell.execute_reply": "2024-05-07T16:07:29.608466Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pyomo.environ as pyo\n", - "\n", - "\n", - "def define_knapsack_model(weights, values, max_weight):\n", - " model = pyo.ConcreteModel()\n", - " num_items = len(weights)\n", - "\n", - " model.x = pyo.Var(range(num_items), domain=pyo.Binary)\n", - "\n", - " x_variables = np.array(list(model.x.values()))\n", - "\n", - " model.weight_constraint = pyo.Constraint(expr=x_variables @ weights == max_weight)\n", - "\n", - " model.value = pyo.Objective(expr=x_variables @ values, sense=pyo.maximize)\n", - "\n", - " return model" - ] - }, - { - "cell_type": "markdown", - "id": "3496c5d6-7df6-49fb-b5b9-5ba48d2b7d62", - "metadata": {}, - "source": [ - "### Initialize the model with parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "62a98e1c-5dbe-42f9-989e-83ee1df7196b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:29.613987Z", - "iopub.status.busy": "2024-05-07T16:07:29.613427Z", - "iopub.status.idle": "2024-05-07T16:07:29.621699Z", - "shell.execute_reply": "2024-05-07T16:07:29.621009Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "knapsack_model = define_knapsack_model(\n", - " weights=[2, 3, 2.1, 1, 1, 2], values=[3, 5, 2, 1.5, 1.2, 2.7], max_weight=5\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c78b22a0-9420-44bf-a60d-a7ce136dcaaf", - "metadata": { - "tags": [] - }, - "source": [ - "## Setting Up the Classiq Problem Instance\n", - "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "947293d1-ac9d-41aa-a162-f60ee16608dd", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:29.626219Z", - "iopub.status.busy": "2024-05-07T16:07:29.625007Z", - "iopub.status.idle": "2024-05-07T16:07:32.195601Z", - "shell.execute_reply": "2024-05-07T16:07:32.194974Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", - "\n", - "qaoa_config = QAOAConfig(num_layers=5)" - ] - }, - { - "cell_type": "markdown", - "id": "fdfcb551-77c3-40f4-a54e-4231c7775f19", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "311e4855-403b-4f3d-a64e-7147be629470", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:32.198391Z", - "iopub.status.busy": "2024-05-07T16:07:32.198019Z", - "iopub.status.idle": "2024-05-07T16:07:32.201281Z", - "shell.execute_reply": "2024-05-07T16:07:32.200682Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)" - ] - }, - { - "cell_type": "markdown", - "id": "0f153f82-829a-44df-98c3-47f693d984df", - "metadata": {}, - "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fb32b921-1a7d-4c79-9cb0-6993b0eb6b6c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:32.203585Z", - "iopub.status.busy": "2024-05-07T16:07:32.203227Z", - "iopub.status.idle": "2024-05-07T16:07:32.322318Z", - "shell.execute_reply": "2024-05-07T16:07:32.321660Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=knapsack_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "78d21bb2-0ac5-471e-a1c9-fb77df0400e7", - "metadata": {}, - "source": [ - "We also set the quantum backend we want to execute on:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cc69a9e9-38d5-4c91-93dd-ad43bab0ca6d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:32.325437Z", - "iopub.status.busy": "2024-05-07T16:07:32.324838Z", - "iopub.status.idle": "2024-05-07T16:07:32.335989Z", - "shell.execute_reply": "2024-05-07T16:07:32.335376Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", - "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a0ee5250-26c9-4329-a69e-d8075d05ebc6", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:32.338229Z", - "iopub.status.busy": "2024-05-07T16:07:32.338050Z", - "iopub.status.idle": "2024-05-07T16:07:32.351757Z", - "shell.execute_reply": "2024-05-07T16:07:32.351182Z" - } - }, - "outputs": [], - "source": [ - "write_qmod(qmod, \"knapsack_binary\")" - ] - }, - { - "cell_type": "markdown", - "id": "1a47c1c9-1b39-4c3a-b291-7e2082fb592e", - "metadata": {}, - "source": [ - "## Synthesizing the QAOA Circuit and Solving the Problem\n", - "\n", - "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "10f219ca-2836-4573-9764-70f39b685fca", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:32.354433Z", - "iopub.status.busy": "2024-05-07T16:07:32.353950Z", - "iopub.status.idle": "2024-05-07T16:07:36.930198Z", - "shell.execute_reply": "2024-05-07T16:07:36.929444Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/db610424-2d79-4db4-8c6c-17734c34b003?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], - "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" - ] - }, - { - "cell_type": "markdown", - "id": "ce31eb36-c7df-4a0d-9e89-5ef477cc1a8c", - "metadata": {}, - "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c862d5d4-6d4a-4251-a0e1-d2d55b3a37f3", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:36.941434Z", - "iopub.status.busy": "2024-05-07T16:07:36.941056Z", - "iopub.status.idle": "2024-05-07T16:07:43.555821Z", - "shell.execute_reply": "2024-05-07T16:07:43.555252Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "result = execute(qprog).result_value()" - ] - }, - { - "cell_type": "markdown", - "id": "d2da3c33-14fe-42c3-ac39-6d4e412ceb74", - "metadata": {}, - "source": [ - "We can check the convergence of the run:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9172ab29-cfd7-4451-a992-02e50e2ec0ff", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:43.560073Z", - "iopub.status.busy": "2024-05-07T16:07:43.559090Z", - "iopub.status.idle": "2024-05-07T16:07:43.585488Z", - "shell.execute_reply": "2024-05-07T16:07:43.584830Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result.convergence_graph" - ] - }, - { - "cell_type": "markdown", - "id": "2d1824ea-80be-4f20-b7ce-d4836483c33f", - "metadata": { - "tags": [] - }, - "source": [ - "# Optimization Results" - ] - }, - { - "cell_type": "markdown", - "id": "2736d59a-20db-4d26-9880-affa35b1e4ef", - "metadata": {}, - "source": [ - "We can also examine the statistics of the algorithm:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8b90f0c5-da3a-467e-be89-509ce00ccaea", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:43.587793Z", - "iopub.status.busy": "2024-05-07T16:07:43.587613Z", - "iopub.status.idle": "2024-05-07T16:07:43.615672Z", - "shell.execute_reply": "2024-05-07T16:07:43.614990Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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probabilitycostsolutioncount
620.0038.0[1, 1, 0, 0, 0, 0]3
240.0177.7[0, 1, 0, 1, 1, 0]17
590.0077.7[0, 1, 0, 0, 0, 1]7
20.0287.5[1, 1, 0, 1, 0, 0]28
110.0247.2[0, 1, 0, 1, 0, 1]24
\n", - "
" - ], - "text/plain": [ - " probability cost solution count\n", - "62 0.003 8.0 [1, 1, 0, 0, 0, 0] 3\n", - "24 0.017 7.7 [0, 1, 0, 1, 1, 0] 17\n", - "59 0.007 7.7 [0, 1, 0, 0, 0, 1] 7\n", - "2 0.028 7.5 [1, 1, 0, 1, 0, 0] 28\n", - "11 0.024 7.2 [0, 1, 0, 1, 0, 1] 24" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " knapsack_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)" - ] - }, - { - "cell_type": "markdown", - "id": "b5c79c0e-6be3-4a27-97dd-3da93dbae5e6", - "metadata": {}, - "source": [ - "And the histogram:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2709135d-366d-4f27-8eff-4dd28a4545e5", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:43.618252Z", - "iopub.status.busy": "2024-05-07T16:07:43.617881Z", - "iopub.status.idle": "2024-05-07T16:07:43.813012Z", - "shell.execute_reply": "2024-05-07T16:07:43.812308Z" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[]], dtype=object)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" - ] - }, - { - "cell_type": "markdown", - "id": "2004e2d2-bcdb-43f8-b068-b8cf5b2beb95", - "metadata": {}, - "source": [ - "Lastly, we can compare to the classical solution of the problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "777c97cb-00c6-46e5-a145-a22f511d66c0", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T16:07:43.815782Z", - "iopub.status.busy": "2024-05-07T16:07:43.815309Z", - "iopub.status.idle": "2024-05-07T16:07:43.864718Z", - "shell.execute_reply": "2024-05-07T16:07:43.863986Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model unknown\n", - "\n", - " Variables:\n", - " x : Size=6, Index=x_index\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : 0.9999999999999998 : 1 : False : False : Binary\n", - " 1 : 0 : 1.0 : 1 : False : False : Binary\n", - " 2 : 0 : 0.0 : 1 : False : False : Binary\n", - " 3 : 0 : 0.0 : 1 : False : False : Binary\n", - " 4 : 0 : 0.0 : 1 : False : False : Binary\n", - " 5 : 0 : 2.220446049250313e-16 : 1 : False : False : Binary\n", - "\n", - " Objectives:\n", - " value : Size=1, Index=None, Active=True\n", - " Key : Active : Value\n", - " None : True : 8.0\n", - "\n", - " Constraints:\n", - " weight_constraint : Size=1\n", - " Key : Lower : Body : Upper\n", - " None : 5.0 : 5.0 : 5.0\n" - ] - } - ], - "source": [ - "from pyomo.opt import SolverFactory\n", - "\n", - "solver = SolverFactory(\"couenne\")\n", - "solver.solve(knapsack_model)\n", - "\n", - "knapsack_model.display()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - }, - "vscode": { - "interpreter": { - "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/applications/optimization/knapsack_binary/knapsack_binary.metadata.json b/applications/optimization/knapsack_binary/knapsack_binary.metadata.json deleted file mode 100644 index f3da2e957..000000000 --- a/applications/optimization/knapsack_binary/knapsack_binary.metadata.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "friendly_name": "Knapsack: Binary Variables", - "description": "Solving Knapsack with Binary Variables Using the QAOA Algorithm", - "problem_domain_tags": ["optimization"], - "qmod_type": ["application"], - "level": ["demos"] -} diff --git a/applications/optimization/knapsack_binary/knapsack_binary.qmod b/applications/optimization/knapsack_binary/knapsack_binary.qmod deleted file mode 100644 index af98415ef..000000000 --- a/applications/optimization/knapsack_binary/knapsack_binary.qmod +++ /dev/null @@ -1,265 +0,0 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=4.61 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=-1.31 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.35 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=-0.7 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=-0.8 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.85 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=4.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.1 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.1 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=4.2 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=4.2 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=6.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=6.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=6.3 - } -]; - -qfunc main(params_list: real[10], output target: qbit[6]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); -} - -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=True, -initial_point=[0.0, 0.12820512820512817, 0.03205128205128204, 0.09615384615384612, 0.06410256410256408, 0.06410256410256408, 0.09615384615384612, 0.03205128205128204, 0.12820512820512817, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=60, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) - -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` diff --git a/applications/optimization/knapsack_binary/knapsack_binary.synthesis_options.json b/applications/optimization/knapsack_binary/knapsack_binary.synthesis_options.json deleted file mode 100644 index 0967ef424..000000000 --- a/applications/optimization/knapsack_binary/knapsack_binary.synthesis_options.json +++ /dev/null @@ -1 +0,0 @@ -{} From bcc9122b26108c811a94916df089c983716aa522 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Sun, 8 Dec 2024 15:28:25 +0200 Subject: [PATCH 17/38] with max_k_vertex_cover --- .../max_k_vertex_cover.ipynb | 430 +++------ .../max_k_vertex_cover.qmod | 882 +----------------- .../max_k_vertex_cover.synthesis_options.json | 44 +- 3 files changed, 191 insertions(+), 1165 deletions(-) diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb index 23e165fe7..a299238ad 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb @@ -50,12 +50,6 @@ "execution_count": 1, "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:01.828768Z", - "iopub.status.busy": "2024-05-07T15:49:01.828308Z", - "iopub.status.idle": "2024-05-07T15:49:02.672170Z", - "shell.execute_reply": "2024-05-07T15:49:02.671553Z" - }, "tags": [] }, "outputs": [], @@ -82,12 +76,6 @@ "execution_count": 2, "id": "48889b21-557b-481c-80c5-3c0b5c91adb6", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:02.675329Z", - "iopub.status.busy": "2024-05-07T15:49:02.674765Z", - "iopub.status.idle": "2024-05-07T15:49:02.679496Z", - "shell.execute_reply": "2024-05-07T15:49:02.678928Z" - }, "tags": [] }, "outputs": [], @@ -115,26 +103,19 @@ "\n", "- Index set declarations (model.Nodes, model.Arcs).\n", "- Binary variable declaration for each node (model.x) indicating whether the variable is chosen for the set.\n", - "- Constraint rule \u2013 ensures that the set is of size k.\n", - "- Objective rule \u2013 counts the number of edges not covered; i.e., both related variables are zero." + "- Constraint rule – ensures that the set is of size k.\n", + "- Objective rule – counts the number of edges not covered; i.e., both related variables are zero." ] }, { "cell_type": "code", "execution_count": 3, "id": "439b4081-cb00-4a59-bc23-20a21a291f67", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:02.681807Z", - "iopub.status.busy": "2024-05-07T15:49:02.681487Z", - "iopub.status.idle": "2024-05-07T15:49:03.007232Z", - "shell.execute_reply": "2024-05-07T15:49:03.006530Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -158,12 +139,7 @@ "execution_count": 4, "id": "345330b2-9c14-41f6-b4ba-e11fb9ca1565", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:03.009901Z", - "iopub.status.busy": "2024-05-07T15:49:03.009417Z", - "iopub.status.idle": "2024-05-07T15:49:03.015488Z", - "shell.execute_reply": "2024-05-07T15:49:03.014894Z" - }, + "scrolled": true, "tags": [] }, "outputs": [ @@ -210,102 +186,80 @@ }, { "cell_type": "markdown", - "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29", + "id": "b85b6a8c-d327-4043-b082-44d82135b51b", "metadata": { "tags": [] }, "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { "cell_type": "code", "execution_count": 5, - "id": "816b468f-a59f-4f2f-8337-4a9d66548425", + "id": "9709fd60-fb81-4af0-882b-efa216477fec", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:03.018150Z", - "iopub.status.busy": "2024-05-07T15:49:03.017651Z", - "iopub.status.idle": "2024-05-07T15:49:05.001488Z", - "shell.execute_reply": "2024-05-07T15:49:05.000681Z" - }, "tags": [] }, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "qaoa_config = QAOAConfig(num_layers=3)" - ] - }, - { - "cell_type": "markdown", - "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" + "combi = CombinatorialProblem(pyo_model=mvc_model, num_layers=3, penalty_factor=10)\n", + "\n", + "qmod = combi.get_model()" ] }, { "cell_type": "code", "execution_count": 6, - "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:05.006362Z", - "iopub.status.busy": "2024-05-07T15:49:05.005049Z", - "iopub.status.idle": "2024-05-07T15:49:05.009888Z", - "shell.execute_reply": "2024-05-07T15:49:05.009276Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, + "id": "133ddfff-b7f4-436a-b656-81a32a236a98", + "metadata": {}, "outputs": [], "source": [ - "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)" + "write_qmod(qmod, \"max_k_vertex_cover\")" ] }, { "cell_type": "markdown", - "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0", + "id": "411eeb04-f598-4585-87bf-a168787714f3", "metadata": {}, "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" + "## Synthesizing the QAOA Circuit and Solving the Problem\n", + "\n", + "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" ] }, { "cell_type": "code", "execution_count": 7, - "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c", + "id": "69d7c8ac-29c6-42b7-9111-fcf88a4e816a", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:05.014101Z", - "iopub.status.busy": "2024-05-07T15:49:05.012999Z", - "iopub.status.idle": "2024-05-07T15:49:05.454332Z", - "shell.execute_reply": "2024-05-07T15:49:05.453564Z" - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/9e9127b4-7c66-4b81-b66c-57745580a2ec?version=0.61.0.dev8\n" + ] + } + ], "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=mvc_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" + "qprog = combi.get_qprog()\n", + "show(qprog)" ] }, { "cell_type": "markdown", - "id": "1fcc3812-c9d0-421c-84bb-38047297b33f", + "id": "7300af19-c7c4-4e93-a01b-fa7305a60b4d", "metadata": {}, "source": [ "We also set the quantum backend we want to execute on:" @@ -314,111 +268,43 @@ { "cell_type": "code", "execution_count": 8, - "id": "53bc041f-065c-44d2-b220-dafd9d0504ac", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:05.459516Z", - "iopub.status.busy": "2024-05-07T15:49:05.458347Z", - "iopub.status.idle": "2024-05-07T15:49:05.490549Z", - "shell.execute_reply": "2024-05-07T15:49:05.489834Z" - }, - "tags": [] - }, + "id": "c052e252-745c-4b93-8df7-992d3c5d3277", + "metadata": {}, "outputs": [], "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", + "from classiq.execution import *\n", "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n", ")" ] }, - { - "cell_type": "code", - "execution_count": 9, - "id": "91fea2e9-0ce2-43cb-850c-3ba65a8a76c4", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:05.495462Z", - "iopub.status.busy": "2024-05-07T15:49:05.494337Z", - "iopub.status.idle": "2024-05-07T15:49:05.546829Z", - "shell.execute_reply": "2024-05-07T15:49:05.546110Z" - } - }, - "outputs": [], - "source": [ - "write_qmod(qmod, \"max_k_vertex_cover\")" - ] - }, { "cell_type": "markdown", - "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6", + "id": "62a5d189-4c7d-42d9-8c8b-5a24d0ed2271", "metadata": {}, "source": [ - "## Synthesizing the QAOA Circuit and Solving the Problem\n", - "\n", - "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" + "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", + "execution_count": 22, + "id": "066869ce-6c80-4e8a-9943-77ceabe74388", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:05.552136Z", - "iopub.status.busy": "2024-05-07T15:49:05.550905Z", - "iopub.status.idle": "2024-05-07T15:49:10.398718Z", - "shell.execute_reply": "2024-05-07T15:49:10.398036Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/308cc52e-d608-47d6-9ccb-ceb9f2e932a2?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], - "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" - ] - }, - { - "cell_type": "markdown", - "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304", - "metadata": {}, - "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:10.401335Z", - "iopub.status.busy": "2024-05-07T15:49:10.400809Z", - "iopub.status.idle": "2024-05-07T15:49:20.990694Z", - "shell.execute_reply": "2024-05-07T15:49:20.990084Z" - }, "tags": [] }, "outputs": [], "source": [ - "result = execute(qprog).result_value()" + "cost_values = []\n", + "optimized_params = combi.optimize(\n", + " execution_preferences, maxiter=90, cost_trace=cost_values, quantile=0.7\n", + ")" ] }, { "cell_type": "markdown", - "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6", + "id": "ab153f29-2a25-422f-aed8-69283f6691e0", "metadata": {}, "source": [ "We can check the convergence of the run:" @@ -426,33 +312,41 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "02454398-b229-403c-824a-b1eb539fbc1f", + "execution_count": 23, + "id": "be6bc7ec-c1f8-4925-be2b-0dceb20d73cc", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:20.993567Z", - "iopub.status.busy": "2024-05-07T15:49:20.993008Z", - "iopub.status.idle": "2024-05-07T15:49:21.013136Z", - "shell.execute_reply": "2024-05-07T15:49:21.012480Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ - "" + "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 12, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.convergence_graph" + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(nrows=1, ncols=1)\n", + "axes.plot(cost_values)\n", + "axes.set_xlabel(\"Iterations\")\n", + "axes.set_ylabel(\"Cost\")\n", + "axes.set_title(\"Cost convergence\")" ] }, { @@ -467,25 +361,17 @@ }, { "cell_type": "markdown", - "id": "670eddd3-2da7-4a88-b571-7884ef24f60c", + "id": "0e49c243-8621-4893-9033-ddb45087f30d", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm:" + "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `get_results` method:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "516d78ba-2951-46eb-b1af-efe877513556", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.015680Z", - "iopub.status.busy": "2024-05-07T15:49:21.015305Z", - "iopub.status.idle": "2024-05-07T15:49:21.093102Z", - "shell.execute_reply": "2024-05-07T15:49:21.092387Z" - }, - "tags": [] - }, + "execution_count": 24, + "id": "778d3344-c84e-47cd-89b6-55d1df980813", + "metadata": {}, "outputs": [ { "data": { @@ -508,83 +394,68 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", - " 0\n", - " 0.058\n", - " 1.0\n", - " [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]\n", - " 58\n", + " 128\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", + " 0.002441\n", + " 2.0\n", " \n", " \n", - " 2\n", - " 0.024\n", - " 1.0\n", - " [1, 1, 0, 1, 1, 0, 0, 0, 1, 0]\n", - " 24\n", + " 335\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", + " 0.000977\n", + " 2.0\n", " \n", " \n", - " 272\n", - " 0.001\n", + " 204\n", + " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", + " 0.001953\n", " 2.0\n", - " [0, 1, 1, 0, 1, 0, 1, 0, 1, 0]\n", - " 1\n", " \n", " \n", - " 220\n", - " 0.001\n", + " 191\n", + " {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", + " 0.001953\n", " 2.0\n", - " [1, 1, 0, 1, 1, 1, 0, 0, 0, 0]\n", - " 1\n", " \n", " \n", - " 156\n", - " 0.002\n", + " 116\n", + " {'x_0': 1, 'x_1': 0, 'x_2': 1, 'x_3': 0, 'x_4'...\n", + " 0.002441\n", " 2.0\n", - " [1, 1, 0, 0, 1, 1, 0, 0, 1, 0]\n", - " 2\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "0 0.058 1.0 [1, 1, 0, 0, 1, 0, 1, 0, 1, 0] 58\n", - "2 0.024 1.0 [1, 1, 0, 1, 1, 0, 0, 0, 1, 0] 24\n", - "272 0.001 2.0 [0, 1, 1, 0, 1, 0, 1, 0, 1, 0] 1\n", - "220 0.001 2.0 [1, 1, 0, 1, 1, 1, 0, 0, 0, 0] 1\n", - "156 0.002 2.0 [1, 1, 0, 0, 1, 1, 0, 0, 1, 0] 2" + " solution probability cost\n", + "128 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.002441 2.0\n", + "335 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.000977 2.0\n", + "204 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.001953 2.0\n", + "191 {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.001953 2.0\n", + "116 {'x_0': 1, 'x_1': 0, 'x_2': 1, 'x_3': 0, 'x_4'... 0.002441 2.0" ] }, - "execution_count": 13, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " mvc_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)" + "optimization_result = combi.get_results()\n", + "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "687f492b-a4a5-49c6-964c-8959b035bb93", + "id": "ea88e99f-300d-4a18-b122-d0f2b31080a8", "metadata": {}, "source": [ "And the histogram:" @@ -592,31 +463,15 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", + "execution_count": 25, + "id": "26f85e77-a110-4e9c-a8ac-3354eae3c8da", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.095943Z", - "iopub.status.busy": "2024-05-07T15:49:21.095378Z", - "iopub.status.idle": "2024-05-07T15:49:21.326954Z", - "shell.execute_reply": "2024-05-07T15:49:21.326200Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/plain": [ - "array([[]], dtype=object)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -626,7 +481,12 @@ } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + ")\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" ] }, { @@ -639,42 +499,30 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.329517Z", - "iopub.status.busy": "2024-05-07T15:49:21.329322Z", - "iopub.status.idle": "2024-05-07T15:49:21.332804Z", - "shell.execute_reply": "2024-05-07T15:49:21.332219Z" - }, "tags": [] }, "outputs": [], "source": [ - "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]" + "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", + "best_solution = [best_solution[f\"x_{v}\"] for v in range(len(best_solution))]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 27, "id": "2449caf6-d3c2-49b1-81cd-0e33e248cc18", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.335350Z", - "iopub.status.busy": "2024-05-07T15:49:21.334887Z", - "iopub.status.idle": "2024-05-07T15:49:21.339246Z", - "shell.execute_reply": "2024-05-07T15:49:21.338598Z" - } - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 1, 0, 0, 1, 0, 1, 0, 1, 0]" + "[1, 1, 1, 0, 0, 0, 1, 0, 1, 0]" ] }, - "execution_count": 16, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -685,20 +533,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "id": "fed415f4-67ed-4a85-9138-553c75972ac8", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.341589Z", - "iopub.status.busy": "2024-05-07T15:49:21.341144Z", - "iopub.status.idle": "2024-05-07T15:49:21.493854Z", - "shell.execute_reply": "2024-05-07T15:49:21.493175Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -744,15 +585,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 29, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.496514Z", - "iopub.status.busy": "2024-05-07T15:49:21.496111Z", - "iopub.status.idle": "2024-05-07T15:49:21.561467Z", - "shell.execute_reply": "2024-05-07T15:49:21.560785Z" - }, "pycharm": { "name": "#%%\n" }, @@ -769,16 +604,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 30, "id": "1894641b-b166-47da-a3b8-5851d9042054", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.564260Z", - "iopub.status.busy": "2024-05-07T15:49:21.563927Z", - "iopub.status.idle": "2024-05-07T15:49:21.568416Z", - "shell.execute_reply": "2024-05-07T15:49:21.567758Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -786,7 +614,7 @@ "[1, 1, 0, 1, 1, 0, 0, 0, 1, 0]" ] }, - "execution_count": 19, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -797,21 +625,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 31, "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:49:21.570739Z", - "iopub.status.busy": "2024-05-07T15:49:21.570457Z", - "iopub.status.idle": "2024-05-07T15:49:21.725756Z", - "shell.execute_reply": "2024-05-07T15:49:21.724890Z" - }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -861,7 +683,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod index e66df54a8..405b870cb 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod @@ -1,863 +1,25 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=10.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=0.75 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.75 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.75 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.75 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.75 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.25 - } -]; - -qfunc main(params_list: real[6], output target: qbit[10]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + x_0: qbit; + x_1: qbit; + x_2: qbit; + x_3: qbit; + x_4: qbit; + x_5: qbit; + x_6: qbit; + x_7: qbit; + x_8: qbit; + x_9: qbit; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=False, -initial_point=[0.0, 0.4, 0.2, 0.2, 0.4, 0.0], -optimizer=Optimizer.COBYLA, -max_iteration=60, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[6], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 3) { + phase (-(((((((((((((((((((((((1 - v.x_0) * (1 - v.x_1)) + ((1 - v.x_0) * (1 - v.x_5))) + ((1 - v.x_0) * (1 - v.x_6))) + ((1 - v.x_0) * (1 - v.x_7))) + ((1 - v.x_0) * (1 - v.x_8))) + ((1 - v.x_1) * (1 - v.x_2))) + ((1 - v.x_1) * (1 - v.x_4))) + ((1 - v.x_1) * (1 - v.x_5))) + ((1 - v.x_1) * (1 - v.x_6))) + ((1 - v.x_1) * (1 - v.x_9))) + ((1 - v.x_2) * (1 - v.x_3))) + ((1 - v.x_2) * (1 - v.x_4))) + ((1 - v.x_3) * (1 - v.x_6))) + ((1 - v.x_3) * (1 - v.x_8))) + ((1 - v.x_4) * (1 - v.x_6))) + ((1 - v.x_4) * (1 - v.x_7))) + ((1 - v.x_4) * (1 - v.x_8))) + ((1 - v.x_4) * (1 - v.x_9))) + ((1 - v.x_5) * (1 - v.x_6))) + ((1 - v.x_7) * (1 - v.x_8))) + ((1 - v.x_8) * (1 - v.x_9))) + (20 * (((((((((((v.x_0 + v.x_1) + v.x_2) + v.x_3) + v.x_4) + v.x_5) + v.x_6) + v.x_7) + v.x_8) + v.x_9) - 5) ** 2))), params[i]); + apply_to_all(lambda(q) { + RX(params[3 + i], q); + }, v); + } +} diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json index 0967ef424..2d6c6696b 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "u", + "s", + "r", + "rx", + "cy", + "sdg", + "z", + "ry", + "rz", + "sx", + "cz", + "h", + "cx", + "x", + "sxdg", + "tdg", + "t", + "p", + "y", + "u2", + "u1", + "id" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 2694895972 + } +} From 52891f117734c78d474676ffef865c1a9d00de1e Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Mon, 16 Dec 2024 10:54:24 +0200 Subject: [PATCH 18/38] updated notebooks accord\ing to CR comments and new CombiProblem interface --- .../integer_linear_programming.ipynb | 158 +++++++----- .../integer_linear_programming.qmod | 6 +- ..._linear_programming.synthesis_options.json | 32 +-- .../optimization/max_clique/max_clique.ipynb | 233 ++++++++++++------ .../optimization/max_clique/max_clique.qmod | 16 +- .../max_clique.synthesis_options.json | 32 +-- .../max_k_vertex_cover.ipynb | 170 +++++++------ .../max_k_vertex_cover.qmod | 13 +- .../max_k_vertex_cover.synthesis_options.json | 34 +-- .../set_partition/set_partition.ipynb | 208 +++++++++------- .../set_partition/set_partition.qmod | 13 +- .../set_partition.synthesis_options.json | 28 +-- .../ising_model/ising_model.ipynb | 177 +++++++------ .../ising_model/ising_model.qmod | 9 +- .../ising_model.synthesis_options.json | 32 +-- 15 files changed, 664 insertions(+), 497 deletions(-) diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb index 1ee489202..702813b48 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb @@ -42,7 +42,7 @@ "\n", "# Solving with the Classiq platform\n", "\n", - "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve." + "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a Pyomo model for the optimization problem we would like to solve." ] }, { @@ -52,12 +52,12 @@ "source": [ "## Building the Pyomo model from a graph input\n", "\n", - "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" + "We proceed by defining the Pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "48889b21-557b-481c-80c5-3c0b5c91adb6", "metadata": { "ExecuteTime": { @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "6e5295f4-7ba6-4ff6-8782-1c4c2c7f85e4", "metadata": { "ExecuteTime": { @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "345330b2-9c14-41f6-b4ba-e11fb9ca1565", "metadata": { "ExecuteTime": { @@ -179,12 +179,12 @@ "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm, with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "816b468f-a59f-4f2f-8337-4a9d66548425", "metadata": { "tags": [] @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "62ec28b3-cb49-411a-8c4a-8004fff6c105", "metadata": {}, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", "metadata": { "pycharm": { @@ -234,7 +234,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/ae0c345f-e27c-496d-a3d8-a7eab1675d72?version=0.61.0.dev7\n" + "Opening: https://nightly.platform.classiq.io/circuit/ef888bc5-02f8-4150-b49f-76cd8bbc7ecf?version=0.62.0.dev9\n" ] } ], @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "7d188c69-21d1-4afe-86b1-46229e91a01e", "metadata": {}, "outputs": [], @@ -275,17 +275,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Optimization Progress: 72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 65/90 [03:27<01:19, 3.19s/it]\n" + ] + } + ], "source": [ - "cost_values = []\n", - "optimized_params = combi.optimize(\n", - " execution_preferences, maxiter=90, cost_trace=cost_values, quantile=0.7\n", - ")" + "optimized_params = combi.optimize(execution_preferences, maxiter=90, quantile=0.7)" ] }, { @@ -298,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "02454398-b229-403c-824a-b1eb539fbc1f", "metadata": { "tags": [] @@ -310,13 +315,13 @@ "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -328,11 +333,10 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "fig, axes = plt.subplots(nrows=1, ncols=1)\n", - "axes.plot(cost_values)\n", - "axes.set_xlabel(\"Iterations\")\n", - "axes.set_ylabel(\"Cost\")\n", - "axes.set_title(\"Cost convergence\")" + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" ] }, { @@ -350,7 +354,7 @@ "id": "670eddd3-2da7-4a88-b571-7884ef24f60c", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the pyomo to qmod translator." + "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the Pyomo to qmod translator." ] }, { @@ -358,12 +362,12 @@ "id": "c99a7e3e-5203-4893-b970-dc23739f6df2", "metadata": {}, "source": [ - "In order to get samples with the optimized parameters, we call the `get_results` method:" + "In order to get samples with the optimized parameters, we call the `sample` method:" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "9638f749-a60b-4176-a4ea-50d7c2bb986f", "metadata": {}, "outputs": [ @@ -395,33 +399,33 @@ " \n", " \n", " \n", - " 151\n", - " {'x_0': 0, 'x_1': 0, 'x_2': 1}\n", - " 0.001953\n", + " 32\n", + " {'x': [0, 0, 1]}\n", + " 0.004883\n", " -3.000000e+00\n", " \n", " \n", - " 178\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0}\n", - " 0.000977\n", + " 0\n", + " {'x': [0, 1, 0]}\n", + " 0.281738\n", " -2.000000e+00\n", " \n", " \n", - " 11\n", - " {'x_0': 1, 'x_1': 0, 'x_2': 0}\n", - " 0.014648\n", + " 8\n", + " {'x': [1, 0, 0]}\n", + " 0.016113\n", " -1.000000e+00\n", " \n", " \n", - " 4\n", - " {'x_0': 0, 'x_1': 0, 'x_2': 0}\n", - " 0.020020\n", + " 73\n", + " {'x': [0, 0, 0]}\n", + " 0.001465\n", " 1.527468e-150\n", " \n", " \n", - " 68\n", - " {'x_0': 0, 'x_1': 0, 'x_2': 1}\n", - " 0.004395\n", + " 104\n", + " {'x': [0, 0, 1]}\n", + " 0.000977\n", " 7.000000e+00\n", " \n", " \n", @@ -429,43 +433,61 @@ "" ], "text/plain": [ - " solution probability cost\n", - "151 {'x_0': 0, 'x_1': 0, 'x_2': 1} 0.001953 -3.000000e+00\n", - "178 {'x_0': 0, 'x_1': 1, 'x_2': 0} 0.000977 -2.000000e+00\n", - "11 {'x_0': 1, 'x_1': 0, 'x_2': 0} 0.014648 -1.000000e+00\n", - "4 {'x_0': 0, 'x_1': 0, 'x_2': 0} 0.020020 1.527468e-150\n", - "68 {'x_0': 0, 'x_1': 0, 'x_2': 1} 0.004395 7.000000e+00" + " solution probability cost\n", + "32 {'x': [0, 0, 1]} 0.004883 -3.000000e+00\n", + "0 {'x': [0, 1, 0]} 0.281738 -2.000000e+00\n", + "8 {'x': [1, 0, 0]} 0.016113 -1.000000e+00\n", + "73 {'x': [0, 0, 0]} 0.001465 1.527468e-150\n", + "104 {'x': [0, 0, 1]} 0.000977 7.000000e+00" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "optimization_result = combi.get_results()\n", + "optimization_result = combi.sample(optimized_params)\n", "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "687f492b-a4a5-49c6-964c-8959b035bb93", + "id": "f08c8085-b50a-41a1-9359-46413f60739c", "metadata": {}, "source": [ - "And the histogram:" + "We will also want to compare the optimized results to uniformly sampled results:" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", + "execution_count": 13, + "id": "25277397-d7a5-4466-af4c-fee2e17bc8b9", + "metadata": {}, + "outputs": [], + "source": [ + "uniform_result = combi.sample_uniform()" + ] + }, + { + "cell_type": "markdown", + "id": "91831ceb-ca0b-44d5-9a1d-e8c70a139592", + "metadata": {}, + "source": [ + "And compare the histograms:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "77668ce1-64f6-4086-b0fe-597ded8ad0e0", "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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0tDLbY2NjZRhGpfWvuP/++3X//fdXuTwAAKjfXApSmZmZatKkicaNG6fHHntMTZs2rVK9P/7xj4qOjnalaQAAgBrnUpB6++23NXz4cPn6+jpV75577tE999zjStMAAAA1zqVv7cXFxenYsWOVlsvKytKGDRtcaQoAAMDjuBSkEhMTNWPGjErLvfLKK0pMTHSlKQAAAI/jUpAyDMOpwdwAAAB1SbVMyPlbZ86ckb+///VoCgAA4LpxerB5dna2w/uCgoIy264oLS21T2jJRJYAAKCucTpIxcbGymKx2N8vW7ZMy5Ytq7COYRgaMWKE870DAADwYE4HqejoaHuQys7OVmBgoH1m8N/y9fVVixYtNHDgQD366KOu9RQAAMDDOB2krp7uwMvLS4MHD9bbb7/tzj4BAADUCi5NyLlw4ULddNNN7uoLAABAreJSkBo1apS7+gEAAFDrXJfpDwAAAOoip+5IxcXFyWKx6KuvvlLLli0VFxdX5boWi0WHDx92uoMAAACeyqkgdezYMVksFpWUlNjfV9XVUyYAAADUBU4FqaNHj0qSmjdv7vAeAACgPnIqSMXExFT4HgAAoD5hsDkAAIBJBCkAAACTnHq096c//cl0QxaLRW+99Zbp+gAAAJ7GqSCVlpZmuiGCFAAAqGucClILFy6srn4AAADUOk4FKZaEAQAA+D8MNgcAADCJIAUAAGCSU4/23nnnHUnS/fffr5CQEPv7qho5cqRT5QEAADyZU0Fq9OjRslgs6tatm0JCQuzvq4ogBQAA6hKngtTIkSNlsVgUFhbm8B4AAKA+cmkeKVfmlQIAAKjtGGwOAABgEkEKAADAJLcEqR9++EGPPPKI2rZtq+DgYAUFBalNmzZ65JFH9N1337mjCQAAAI/jcpCaO3euOnXqpDfffFMHDx7U+fPnVVRUpKysLL3xxhvq0qWL/vWvf7mjrwAAAB7FpSC1YsUKPfnkkyotLdV///d/69NPP9W3336rb7/9Vv/+9781aNAgXbp0SU8//bRWrFjhrj4DAAB4BKe+tfdbr7zyiiwWi95//30NHjzYYd+tt96qP/zhD/rwww81ZMgQvfLKK+rfv79LnQUAAPAkLt2R2rlzp+Lj48uEqKsNGjRId9xxh3bu3OlKUwAAAB7HpSBlsVjUqlWrSsu1atWKiTsBAECd41KQuu2225SVlVVpuaysLLVv396VpgAAADyOS0HqmWee0fbt2/X++++XWyYjI0Pbt2/X008/7UpTAAAAHsepwebZ2dkO77t06aKnn35aI0aM0IcffqiRI0eqZcuWkqSjR4/q3Xff1UcffaSnn35aXbt2dV+vAQAAPIBTQSo2NvaaY50Mw9BHH32kjz766Jr7XnvtNc2aNUulpaXmewq327dvn6l6ERERio6OdnNvAACofZwKUtHR0QwarwMuFZyRLBaNGDHCVH3/gEAd2L+PMAUAqPecClLHjh2rpm7gerIVF0iGofC7J8gnPMqpuiWnc3T6s1dltVoJUgCAes+lCTlRu/mER8kv8qaa7gYAALWWWxYtBgAAqI/cekcqLy9P+fn5Mgzjmvt5FAQAAOoSl4PUmTNnNGXKFH3wwQf65Zdfyi1nsVj41h4AAKhTXApSeXl56tatmw4dOqQGDRooICBA58+fV9OmTZWbmyvDMGSxWLgTBQAA6iSXxkj9/e9/V1ZWlkaOHKm8vDwNGjRIFotFP/74o86dO6d58+apYcOGSkhI0NGjR93VZwAAAI/g0h2pTz/9VBEREZo3b578/f0d5pgKDAzU+PHj1aFDB/Xs2VPdu3fXuHHjXO4wAACAp3DpjtSRI0fUpUsX+fv7S5I9SF26dMleplu3brrzzjv11ltvudIUAACAx3F5+oNGjRrZ/xwYGCjp8gD0q0VHR2v//v2uNgUAAOBRXApSzZo1048//mh/f2VQ+TfffONQ7siRI/L2Zu5PAABQt7gUpNq3b68DBw7Y3/fq1UuGYWjq1Kk6d+6cJGnRokXatm2bbrnlFtd6CgAA4GFcuk3Ur18/ffTRR1q7dq0SExN15513qkePHtq0aZMaN26s0NBQnT17VhaLRX/5y1/c1Wd4gH379l2XOgAAeDKXgtSwYcN0yy23KDY21r5t+fLleuihh7RixQqdOXNGjRo10pQpU3TPPfe42ld4gEsFZySLRSNGjKjprgAAUONcClLBwcHq0aOHw7YmTZro008/1fnz55WXl6cbb7xRXl4s6VdX2IoLJMNQ+N0T5BMe5VTdoiM7lPf1omrqGQAA11+1jQAPDAy0f4sPdY9PeJT8Im9yqk7J6Zxq6g0AADXDrUEqNzdXJ06ckGEYatGihZo2berOwwMAAHgUtzxze/PNN9W2bVs1b95cd9xxh7p166YWLVqobdu2mj9/vjuaAAAA8DguBSmbzaaUlBQ98sgjOnjwoAzDUOPGjdW4cWMZhqGDBw/qscce0+DBg2Wz2dzVZwAAAI/gUpCaM2eOPvjgA0VERGj27NnKz8/XL7/8ol9++UX5+fmaM2eObrjhBi1fvlxz5sxxV58BAAA8gktB6q233pKfn5/WrVunxx9/XMHBwfZ9wcHBeuyxx7RmzRr5+PhowYIFLncWAADAk7gUpLKysnTXXXepXbt25ZZp166dEhMTdejQIVeaAgAA8DguBang4GCHRYvL06hRI4e7VVW1fft2DRgwQA0bNlRQUJC6deumpUuXVrn+4cOHlZqaqnvvvVfNmzeXxWJxmDy0PJmZmUpISFBISIhCQ0OVmJio1atXO91/AABQt7k0/UHPnj21bds22Wy2cifdtNls2rZtm7p37+7UsdeuXavk5GT5+/tr6NChCgkJ0bJly5SSkqKcnBxNmDCh0mN8/fXXevHFF9WgQQO1a9dOubm5ldZZtGiRHnzwQTVp0kSjR4+WJGVkZKhv375aunSpBg0a5NR5AACAusulO1Kpqak6deqUnnrqKV28eLHM/pKSEj311FPKzc3Viy++WOXjlpaWauzYsfLy8tKGDRv0xhtv6NVXX9XevXt18803a9KkSTp+/Hilx+ndu7e2bNmic+fO6dtvv5WPj0+F5c+cOaMnn3xSERER2rVrl2bPnq3Zs2dr165dCg8P16OPPmpfjBkAAMCpO1LvvPNOmW1jxozR3LlztXz5cg0ZMkQtW7aUJB09elQffPCBTp48qUceeUR79+5Vhw4dqtTOmjVrdPjwYY0ZM0YdO3a0bw8LC9OkSZM0evRopaena8qUKRUeJy4uTnFxcVU+vw8++EBnz57Viy++qBYtWti3t2jRQk888YRSU1P10UcfaeTIkVU+JgAAqLucClKjR4+WxWIps90wDJ08eVKzZs0qs12SXn/9db3++utVDiDr1q2TJCUlJZXZl5ycLElav369M113S7upqalav349QQoAAEhyMkiNHDnymkHK3bKysiRJrVu3LrMvMjJSwcHB9jLXq90r2yprt7i4WMXFxfb3+fn5buwhAADwJE4FqbS0tGrqhqO8vDxJlx/lXUtoaKi9zPVqNzQ01KFMeaZPn+7UeDAAAFB7uWWtPfyfiRMnKi8vz/7Kycmp6S4BAIBq4tL0B79lGIZOnz4tSWrcuHG5UyJU5sodofLu/uTn51dp/ipX2g0PDy/T5tVlyuPn5yc/Pz+39w0AAHget9yRWr16tfr166fg4GDdeOONuvHGGxUSEqL+/fubmsiyovFIubm5KigouOY4JldV1G5F46cAAED95HKQeumll5SUlKSVK1eqqKhIhmHIMAwVFRUpMzNTSUlJevnll506ZkJCgiRp5cqVZfZlZmY6lHGnmmoXAADUTi4Fqa+++kqpqany8fHRE088od27dys/P1/5+fnas2ePnnzySfn6+mrq1Klas2ZNlY/bp08fxcXFacmSJdqzZ499e15enqZNmyZfX1+HKQhOnTql/fv3uzwAfciQIQoLC9Ps2bN14sQJ+/YTJ05ozpw5ioiI0P333+9SGwAAoO5waYzUv/71L1ksFn3yySf2+Z2uuO222zRr1iz94Q9/UP/+/TVr1iz913/9V9U65e2tBQsWKDk5Wb1793ZYIub48eOaOXOmw5p5EydOVHp6uhYuXGhf1kWSrFarnn32Wfv7kpISWa1WhzIzZ85URESEpMtrAs6ZM0cPPvigOnfurJSUFEmXl4g5ffq0MjIyFBIS4uSnBAAA6iqXgtSVNfR+G6KulpSUpO7du2vLli1OHTsxMVEbN27U1KlTlZGRoZKSErVv314zZsywB5zKFBQUKD093WFbYWGhw7bU1FR7kJKkESNGKCIiQtOmTdPChQtlsVjUpUsXTZ48Wb///e+dOgcAAFC3uRSkzp49q5iYmErLxcTE6D//+Y/Tx4+Pj9eKFSsqLZeWlnbNOa5iY2Pts6s7o1+/furXr5/T9QAAQP3i0hipiIgI7d+/v9Jy+/fvd7jrAwAAUBe4FKR69Oih3bt3a8mSJeWWWbx4sXbt2qWePXu60hQAAIDHcenR3nPPPafly5dr5MiR+vjjjzVq1Ci1bNlSknTkyBGlpaXp448/VoMGDRwGfQMAANQFLgWprl27at68eXr88cf14YcfatmyZQ77DcOQt7e35s6dq65du7rUUQAAAE/j8oScY8eO1a5du/SnP/1JcXFx9iVS4uLi9NBDD2nXrl0aO3asO/oKAADgUVy6I5WdnS2LxaLf/e53WrBggbv6BAAAUCu4dEcqNjZWQ4cOdVdfAAAAahWXglRoaKh9cDkAAEB941KQuuWWW5STk+OuvgAAANQqLgWpsWPHatOmTdq+fbu7+gMAAFBruBSkxowZo8cee0xJSUmaNm2aDhw4oOLiYnf1DQAAwKO59K29Bg0a2P/8wgsv6IUXXii3rMViUWlpqSvNAQAAeBSXgpQzCwKbWTwYAADAk7kUpGw2m7v6AQAAUOuYClKHDh3S8uXLdezYMfn5+alTp04aPHiwAgIC3N0/AAAAj+V0kHrttdf0l7/8RZcuXXLYPnnyZH3xxRf63e9+57bOAQAAeDKnvrW3ceNGTZgwQaWlpQoMDFSnTp3UqlUrWSwWnThxQgMHDuRxHwAAqDecClJz5syRYRgaNWqUcnNztWPHDh08eFC7du1Sq1atdOjQIX355ZfV1VcAAACP4lSQ2rJli1q0aKH58+crKCjIvv22227TrFmzZBiGtm7d6vZOAgAAeCKngtRPP/2k22+/Xb6+vmX29ezZU5L0888/u6dnAAAAHs6pIHXx4kU1bNjwmvtCQ0PtZQAAAOoDl5aIAQAAqM+cnv7g0KFDeuedd0ztHzlypLPNAQAAeCyng9SmTZu0adOma+6zWCzl7rdYLAQpAABQpzgVpKKjo2WxWKqrLwAAALWKU0Hq2LFj1dQNAACA2ofB5gAAACYRpAAAAEwiSAEAAJhEkAIAADDJ6ekPAFRddna2rFarqboRERGKjo52c48AAO5EkAKqSXZ2ttq0bacLRedN1fcPCNSB/ftMhSlXApxEiAOAqiJIAdXEarXqQtF5hd89QT7hUU7VLTmdo9OfvSqr1ep0oHE1wEmuhTgAqE8IUkA18wmPkl/kTdetPVcCnORaiAOA+oYgBdRR1zvAAUB9xLf2AAAATCJIAQAAmESQAgAAMIkgBQAAYBJBCgAAwCSCFAAAgEkEKQAAAJMIUgAAACYRpAAAAEwiSAEAAJhEkAIAADCJIAUAAGASQQoAAMAkghQAAIBJBCkAAACTCFIAAAAmEaQAAABMIkgBAACYRJACAAAwybumOwBcD9nZ2bJarabqRkREKDo62s09AgDUBQQp1HnZ2dlq07adLhSdN1XfPyBQB/bvI0wBAMogSKHOs1qtulB0XuF3T5BPeJRTdUtO5+j0Z6/KarUSpAAAZRCkUG/4hEfJL/Kmmu4GAKAOYbA5AACASQQpAAAAkwhSAAAAJhGkAAAATCJIAQAAmESQAgAAMMmjg9T27ds1YMAANWzYUEFBQerWrZuWLl3q1DGKi4v10ksvqXXr1vL391ezZs00btw4/fzzz2XKHjt2TBaLpdxXamqqm84MAADUBR47j9TatWuVnJwsf39/DR06VCEhIVq2bJlSUlKUk5OjCRMmVHoMm82m++67T5mZmerWrZsGDhyorKwsLViwQKtXr9bWrVvVpEmTMvU6dOigP/7xj2W233XXXW44MwAAUFd4ZJAqLS3V2LFj5eXlpQ0bNqhjx46SpClTpig+Pl6TJk3SoEGDFBMTU+Fx0tPTlZmZqWHDhmnx4sWyWCySpNdff12PPvqoJk+erPnz55ep17FjR+4+AQCASnnko701a9bo8OHDGj58uD1ESVJYWJgmTZqkixcvKj09vdLjvPnmm5Kk6dOn20OUJI0fP15xcXFavHixioqK3N5/AABQP3hkkFq3bp0kKSkpqcy+5ORkSdL69esrPMaFCxe0bds2tWnTpsydK4vFor59+6qwsFA7duwoU/fkyZOaO3eupk2bprfeekuHDx82eSYAAKAu88hHe1lZWZKk1q1bl9kXGRmp4OBge5nyHD58WDab7ZrHuPrYWVlZ6tWrl8O+VatWadWqVfb3FotFDzzwgF5//XUFBQVV2G5xcbGKi4vt7/Pz8yssDwAAai+PvCOVl5cn6fKjvGsJDQ21l3HlGFeXk6TAwEC98MIL2rlzp86ePatff/1VX331leLj47Vo0SKNHDmy0r5Pnz5dYWFh9ldUVFSldQAAQO3kkUGqptxwww166aWX1LlzZ4WFhalRo0bq06eP1qxZozZt2mj58uXatWtXhceYOHGi8vLy7K+cnJzr1HsAAHC9eeSjvSt3kcq765Sfn69GjRq5fIyry1UkMDBQDz74oCZPnqxNmzapc+fO5Zb18/OTn59fpccE4F7Z2dmyWq2m6kZERCg6OtrNPQJQH3hkkLp6/FKXLl0c9uXm5qqgoEDx8fEVHiMuLk5eXl7ljqWqaBzWtUREREiSCgsLq1QewPWTnZ2tNm3b6ULReVP1/QMCdWD/PsIUAKd5ZJBKSEjQ9OnTtXLlSg0dOtRhX2Zmpr1MRQICAhQfH6+tW7fq+PHjDt/cMwxDq1atUlBQkG6//fYq9Wnbtm2SpNjYWCfOBMD1YLVadaHovMLvniCfcOfGJZacztHpz16V1WolSAFwmkeOkerTp4/i4uK0ZMkS7dmzx749Ly9P06ZNk6+vr8PA71OnTmn//v1lHuONGzdO0uVxS4Zh2LfPnz9fR44c0QMPPKCAgAD79t27dzuUu2L58uVKT09Xo0aN1L9/f3edJgA38wmPkl/kTU69nA1eAHA1j7wj5e3trQULFig5OVm9e/d2WCLm+PHjmjlzpsOdoYkTJyo9PV0LFy7U6NGj7dtHjRqljIwMvffeezp69KgSEhJ06NAhLV++XC1bttTLL7/s0O7TTz+tw4cP684771SLFi106dIl7dq1Sxs3bpSfn5/S0tKqNKYKAADUDx4ZpCQpMTFRGzdu1NSpU5WRkaGSkhK1b99eM2bMUEpKSpWO4eXlpU8++UR/+9vf9O677+qf//ynGjdurIceekgvv/xymXX2RowYoWXLlmnr1q2yWq2y2Wxq3ry5Hn74YU2YMEFt27atjlMFAAC1lMcGKUmKj4/XihUrKi2XlpamtLS0a+7z8/PT1KlTNXXq1EqP8/DDD+vhhx92tpsAAKCe8sgxUgAAALUBQQoAAMAkghQAAIBJBCkAAACTCFIAAAAmEaQAAABMIkgBAACYRJACAAAwiSAFAABgEkEKAADAJIIUAACASQQpAAAAkwhSAAAAJhGkAAAATCJIAQAAmESQAgAAMIkgBQAAYBJBCgAAwCSCFAAAgEkEKQAAAJMIUgAAACYRpAAAAEwiSAEAAJhEkAIAADDJu6Y7AKBuyc7OltVqNVU3IiJC0dHRbu4RAFQfghQAt8nOzlabtu10oei8qfr+AYE6sH8fYQpArUGQAuA2VqtVF4rOK/zuCfIJj3KqbsnpHJ3+7FVZrVaCFIBagyAFwO18wqPkF3lTTXcDAKodg80BAABMIkgBAACYRJACAAAwiSAFAABgEkEKAADAJIIUAACASQQpAAAAkwhSAAAAJhGkAAAATCJIAQAAmESQAgAAMIkgBQAAYBKLFgNVsG/fvutSBwBQuxCkgApcKjgjWSwaMWJETXcFAOCBCFJABWzFBZJhKPzuCfIJj3KqbtGRHcr7elE19QwA4AkIUkAV+IRHyS/yJqfqlJzOcbldHikCgGcjSAEeiEeKAFA7EKRQq9SXOzQ8UgSA2oEghVqhvt6hqalHigCAqiFIoVbgDg1Qv2VnZ8tqtZqqGxERoejoaDf3CLiMIIVahTs08DT8gq9+2dnZatO2nS4UnTdV3z8gUAf27+OzRrUgSAGASfyCvz6sVqsuFJ03dUe65HSOTn/2qqxWK58zqgVBCgBM4hf89WXmjjRQ3QhSAOAifsED9ReLFgMAAJhEkAIAADCJIAUAAGASY6QAeJT6Mns9gLqBIAXAI9TX2evh2ZgnDJUhSAHwCMxeD0/DPGGoCoIUAI/C7PXwFMwThqogSAEAUIH6NE8YjzKdR5ACAAA8yjSJIAUAAHiUaRJBCgAA2NWnR5nuQJACgHqGcTCA+3h0kNq+fbumTp2qzZs3q6SkRO3bt9czzzyjIUOGVPkYxcXFmjFjht59913l5OSocePGuvvuu/Xyyy/rhhtuuGadxYsXa9asWfr+++/l6+urHj166KWXXlLnzp3ddWoA4BKzYejUqVMaOGiwii8UmWq3vo6DAcrjsUFq7dq1Sk5Olr+/v4YOHaqQkBAtW7ZMKSkpysnJ0YQJEyo9hs1m03333afMzEx169ZNAwcOVFZWlhYsWKDVq1dr69atatKkiUOdv/71r5o8ebJiYmL0yCOP6Ny5c3r//ffVvXt3rV69Wj169KiuUwaAKnF1ULAkxsEAbuKRQaq0tFRjx46Vl5eXNmzYoI4dO0qSpkyZovj4eE2aNEmDBg1STExMhcdJT09XZmamhg0bpsWLF8tisUiSXn/9dT366KOaPHmy5s+fby+flZWl1NRU3XzzzfrPf/6jsLAwSdJjjz2mbt26aezYsfruu+/k5cUShQDcw+ySOGYHBV+ZvJRxMPAktflxs0cGqTVr1ujw4cMaM2aMPURJUlhYmCZNmqTRo0crPT1dU6ZMqfA4b775piRp+vTp9hAlSePHj9ff//53LV68WK+99poCAgIkSQsXLlRpaamef/55e4iSpI4dO2rYsGFKS0vTxo0b1bt3bzeeLYD6yB1L4jB5qeczuw5kTYeD66m2T7vgkUFq3bp1kqSkpKQy+5KTkyVJ69evr/AYFy5c0LZt29SmTZsyd64sFov69u2r+fPna8eOHerVq1eV2k1LS9P69esJUgBcxpI4dZurQbmmw8H1VNunXfDIIJWVlSVJat26dZl9kZGRCg4Otpcpz+HDh2Wz2a55jKuPnZWVZQ9SWVlZCg4OVmRkZIXlK1JcXKzi4mL7+7y8PElSfn5+hfWcVVBQcLm93EOyXbzgVN0r/yOlLnWvWf/XE5KknTt32v+eVdWBAwdMt11jn1cNn6+tpNjpukbpRZfbvd6flSR5eXnJZrM5Xc+lz7mGrm/xyX2SYSi063+rQViTyitc5VLeL8rfvlyZmZlq06aNU3Wl2vs5m/lZsJVc/n1bUFDg9t+zV45nGEbFBQ0P1LdvX0OSkZWVdc39zZo1M0JDQys8xqZNmwxJxgMPPHDN/W+88YYhyfjHP/5h3+bj42M0b978muUPHjxoSDLuvffeCtudOnWqIYkXL168ePHiVQdeOTk5Ff7e98g7UrXZxIkT9cwzz9jf22w2/frrrwoPD3cYp+UO+fn5ioqKUk5OjkJDQ916bNQ8rm/dxvWt+7jGtZthGDp37pyaNWtWYTmPDFJXBnpfeSz2W/n5+WrUqJHLx7i63JU/O1P+Wvz8/OTn5+ewrWHDhhXWcVVoaCg/pHUY17du4/rWfVzj2quy3/mS5JHf469oPFJubq4KCgrKHft0RVxcnLy8vMod03StcVitW7dWQUGBcnNzq1QeAADUbx4ZpBISEiRJK1euLLMvMzPToUx5AgICFB8frwMHDuj48eMO+wzD0KpVqxQUFKTbb7/dre0CAID6wyODVJ8+fRQXF6clS5Zoz5499u15eXmaNm2afH19NXLkSPv2U6dOaf/+/WUey40bN07S5XFLxlWj7ufPn68jR47ogQcesM8hJUljxoyRt7e3/vrXvzoca8+ePXrvvffUrl079ezZ092na5qfn5+mTp1a5lEi6gaub93G9a37uMb1g8UwKvteX80ob4mY48ePa+bMmQ5LxFyZoHPhwoUaPXq0fbvNZtOAAQPsS8QkJCTo0KFDWr58uWJjY7Vt27YKl4gZOHCgfYmYixcvskQMAABw4JF3pCQpMTFRGzduVI8ePZSRkaF58+bpxhtv1Pvvv1+ldfaky3NpfPLJJ0pNTdUvv/yif/7zn9q0aZMeeughbdmypUyIkqTnn39eixYtUpMmTTRv3jwtXbpUvXr10ubNmwlRAADAgcfekQIAAPB0HntHCgAAwNMRpAAAAEwiSNVC27dv14ABA9SwYUMFBQWpW7duWrp0aU13C9cQGxsri8Vyzdddd91VpnxxcbFeeukltW7dWv7+/mrWrJnGjRunn3/+udw2Fi9erPj4eAUFBalRo0a6++67tWvXrmo8q/pn0aJFGj9+vG6//Xb5+fnJYrEoLS2t3PL5+fl65plnFBMTIz8/P8XGxuq5554rdw0ym82m2bNnq3379goICFCTJk00bNgwHTlypNw2MjMzlZCQoJCQEIWGhioxMVGrV6929VTrJWeub2pqark/0xaLRceOHbtmPWev18GDBzVkyBBFREQoICBAHTp00Lx58ypf9w3XnUfObI7ylfdtxpSUFOXk5FR5ID6un7CwMD311FNltsfGxjq8t9lsuu++++zfMh04cKCysrK0YMECrV69Wlu3bq3wW6aPPPKI/Vum3bt351umbjR58mQdP35cERERatq0aZm56a5WWFiohIQE7dmzR0lJSRo2bJh2796tmTNnav369dqwYYP8/f0d6owfP14LFizQrbfeqj//+c86efKkli5dqpUrV2rr1q1lJgJetGiRHnzwQTVp0sT+TeWMjAz17dtXS5cu1aBBg9z+GdRlzlzfK0aNGlXmZ1i69koWzl6vH374Qd27d1dRUZGGDBmiZs2a6fPPP9djjz2mH374QbNnzzZzmqguFa7EB49SUlJitGrVyvDz8zN2795t33727Fnj5ptvNnx9fY1jx47VXAdRRkxMjBETE1Olsm+//bYhyRg2bJhhs9ns2+fNm2dIMsaNG+dQ/uDBg4a3t7dx8803G2fPnrVv3717t+Hn52e0a9fOuHTpklvOo75btWqV/Wdr+vTphiRj4cKF1yw7ZcoUQ5LxP//zPw7b/+d//seQZEybNs1h+5o1awxJRu/evY3i4mL79i+++MKQZCQlJTmU//XXX42GDRsaERERDoup5uTkGBEREUZERISRn5/vyunWO85c3ysL069du7ZKxzZzvXr37m1IMr744gv7tuLiYqNXr16GJGPz5s3OnSCqFUGqFsnMzDQkGWPGjCmzLy0tzZBkvPjiizXQM5THmSB15513GpLKhGGbzWbExcUZQUFBxvnz5+3bJ06caEgy0tPTyxxr9OjRhiRj/fr1LvUfZVX0i9ZmsxnNmjUzgoODjYKCAod9BQUFRnBwsBEXF+ewfdiwYeVeq7vuusuQZBw/fty+bf78+eX+rKemppb7dwJV4+4g5ez1OnDggCHJSExMLFN+3bp15f4OQM1hjFQtsm7dOklSUlJSmX3JycmSpPXr11/PLqEKiouLlZaWpmnTpmnOnDnatm1bmTIXLlzQtm3b1KZNG8XExDjss1gs6tu3rwoLC7Vjxw77dv4+eJ6srCydPHlSPXr0UFBQkMO+oKAg9ejRQ0eOHFFOTo59+7p16+z7futa15Hr7hk2bNigGTNm6O9//7s+/vjjcse/OXu9Kirfs2dPBQUFcX09DGOkapGKFk6OjIxUcHBwuYs0o+bk5uZqzJgxDtu6du2q9957T61atZIkHT58WDabrdxFsa9eyLtXr172PwcHBysyMrLC8rh+KlvcvHXr1srMzFRWVpaioqJUWFioU6dO6Xe/+50aNGhwzfJXH7eyNrju18/UqVMd3jds2FCzZs1yWL5Mcv56VVS+QYMGatmypX744QeVlpbK25tf4Z6AO1K1yJX1/8LCwq65PzQ0tMx6g6hZY8aM0erVq/XTTz+psLBQu3fv1oMPPqjt27erT58+OnfunKSqXdury135szPlUf2cvY5mr3t5dbju1a9Dhw56++23deTIERUVFeno0aOaPXu2LBaLRo8erU8//dShvLPXqyp/J2w2m/3fDtQ84ixQjX77v9aOHTvqnXfekSS9++67evPNN/XMM8/URNcAmHD//fc7vI+NjdUTTzyhdu3aqW/fvpo8ebLuvffeGuodagJ3pGqRK/9DKe9/m/n5+eX+LwaeZfz48ZKkTZs2Saratb263JU/O1Me1c/Z62j2updXh+tec/r06aNWrVrp22+/tV8HyfnrVZW/ExaLRSEhIW7rO1xDkKpFKhr/kJubq4KCgnLHZsCzRERESLo855AkxcXFycvLq9yxLdcaN9G6dWsVFBQoNze3SuVR/Sobo/Tb6xIUFKSmTZvq6NGjunTpUqXlK2uD616zrvxcnz9/3r7N2etVUflLly7p6NGjatmyJeOjPAhBqhZJSEiQJK1cubLMvszMTIcy8GxXvrl3ZUK/gIAAxcfH68CBA2UmAzQMQ6tWrVJQUJBuv/12+3b+Pnie1q1bq1mzZtq0aZM9JF9RWFioTZs2qWXLloqKirJvT0hIsO/7rSvXsXfv3g7lJa67pyksLNT333+voKAge6CSnL9eFZXfuHGjfcJXeJCann8BVVdSUmLExcVVOCHn0aNHa6x/cLRv3z6jsLDwmtsjIyPLzB3k7IScBw4cYELOGuAJE3KGhYUxIWc1qej65ufnGwcOHCiz/fz58/b5wH47x5OZ61XZhJybNm1y8SzhThbDYOGe2qS8JWKOHz+umTNnskSMB0lNTdU//vEP9e7dWzExMQoKCtLBgwf1xRdfqKSkRBMnTtS0adPs5W02mwYMGGBfIiYhIUGHDh3S8uXLFRsbq23btlW4RMzAgQPtS8RcvHiRJWLcaMGCBdq4caMk6dtvv9WuXbvUo0cP3XTTTZIuz+/z8MMPS7p8Z6JHjx7au3evkpKS1LlzZ+3atUsrV65U165dtX79egUEBDgcf+zYsfYlYv7whz/o1KlTysjIUHBwsLZs2aKbb77ZofzVS46kpKRIurzkiNVqVUZGhgYPHlzdH0mdUtXre+zYMcXFxalr165q166dIiMj9dNPP+mrr77SiRMn1L59e61du1bh4eEOx3f2en3//ffq0aOHioqKlJKSoqZNm+rzzz/X999/ryeeeIIlYjxNTSc5OG/btm1Gv379jNDQUCMgIMCIj4833n///ZruFn5j3bp1xpAhQ4zWrVsboaGhhre3txEZGWncd999RmZm5jXrXLhwwUhNTTVatWpl+Pr6GpGRkcbDDz9s5ObmltvOokWLjNtvv90ICAgwwsLCjAEDBhg7d+6srtOql0aNGmVIKvc1atQoh/Jnz541nnrqKSMqKsrw8fExoqOjjQkTJpR7p+jSpUvGrFmzjFtvvdXw8/MzwsPDjZSUFOPQoUPl9mnFihVGr169jKCgICM4ONhISEgwVq1a5c7Trjeqen3z8vKMxx9/3OjatavRpEkTw9vb2wgJCTHi4+ONV155xWHlgd9y9nrt37/fGDRokNG4cWPDz8/PaN++vTF37lyHu9XwDNyRAgAAMInB5gAAACYRpAAAAEwiSAEAAJhEkAIAADCJIAUAAGASQQoAAMAkghQAAIBJBCkAAACTCFIAAAAmEaQAAABMIkgBQA1JS0uTxWLR6NGja7orAEwiSAEAAJhEkAIAADCJIAWgXjl//rxee+019ezZU40aNZKfn59iYmJ0zz33aMmSJWXK/u1vf1Pnzp0VEhKiwMBA3XrrrZo8ebLOnDlzzePv3LlTKSkpatGihXx9fRUaGqq4uDgNHDhQn3zyib1cbGysxowZI0lKT0+XxWKxv+66665qO38A7mUxDMOo6U4AwPWQk5Ojfv366YcfflBgYKB69Oih8PBw/fjjj/rmm2/UsGFDHTt2TJL066+/qk+fPtqzZ49CQ0N11113ycfHR+vXr5fValXLli21Zs0axcbG2o+/evVq9e/fXyUlJerQoYNat26tS5cu6ccff9TevXvVr18/ffzxx5KkZ599Vlu3btWmTZvUqlUr9ezZ036ctm3b6v/9v/93HT8ZAKYZAFAPXLp0ybj99tsNSUZSUpLx888/O+wvKioyPv/8c/v7lJQUQ5Jxxx13GFar1b793LlzRv/+/Q1JRvfu3R2OkZiYaEgyFi1aVKb9s2fPGlu2bHHYtnDhQkOSMWrUKDecIYCawKM9APXCv//9b+3YsUNNmzbVsmXL1KRJE4f9/v7+GjBggCQpOztbH3zwgSwWi9544w2Fh4fbywUHB+vNN9+Uv7+/Nm/erM2bN9v3/fTTT5JkP87VwsLC1K1bt+o4NQA1iCAFoF748ssvJUnDhw9XcHBwhWU3bNggm82mTp066bbbbiuzv3nz5kpOTpYkrV271r49Pj5ekvTAAw9o48aNKi0tdVf3AXgoghSAeuH48eOSLo8/qsyPP/4oSWrZsmW5ZVq1auVQVpKmT5+uzp07a8WKFerVq5dCQ0PVs2dPTZ48Wfv27XOl+wA8FEEKANwkMjJSO3bs0Nq1a/X888/rjjvu0K5du/TXv/5Vt956q2bMmFHTXQTgZgQpAPVCdHS0JGn//v2Vlm3evLkk6ciRI+WWubLvStkrrkxf8PLLL2vt2rX69ddfNW/ePFksFk2aNEmHDx82ewoAPBBBCkC90K9fP0nSe++9p8LCwgrL9u7dW15eXtqzZ4/27t1bZv+pU6fsY64SExMrPJa/v78eeeQR3XbbbbLZbPrmm2/s+3x9fSWJsVRALUaQAlAv3HvvverUqZNOnjypwYMH6/Tp0w77L1y4oBUrVki6fPdq8ODBMgxD48ePdyhbWFiocePG6cKFC+revbu6d+9u3zdz5kxlZ2eXaXv//v3KysqSJMXExNi3t2jRQpL0ww8/uO9EAVxXTMgJoN44fvy4kpOTdeDAAQUGBqpnz572CTn37t3rMCHn6dOn1adPH+3du1dhYWFKTEyUt7e31q9fr19++eWaE3I2bNhQeXl5atu2rdq1a6eAgACdPHnS/g2+kSNHKj093V7+4sWLatmypU6ePKlOnTqpffv28vHxUZs2bfTcc89d508HgBkEKQD1SkFBgf73f/9XH374ofbv36+LFy8qMjJSHTp00PDhw5WSkmIve/78ef3rX/9SRkaGDh48KJvNppYtW+r+++/Xs88+q0aNGjkce/HixVq9erW2b9+ukydPqrCwUJGRkbrllls0btw43XfffbJYLA51vvvuOz3//PPasmWLTp8+LZvNpoSEBK1bt+56fBwAXESQAgAAMIkxUgAAACYRpAAAAEwiSAEAAJhEkAIAADCJIAUAAGASQQoAAMAkghQAAIBJBCkAAACTCFIAAAAmEaQAAABMIkgBAACYRJACAAAw6f8D7wbOYTuwX0AAAAAASUVORK5CYII=", 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", 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" ] @@ -476,8 +498,22 @@ ], "source": [ "optimization_result[\"cost\"].plot(\n", - " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + " kind=\"hist\",\n", + " bins=30,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", + ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=30,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", ")\n", + "plt.legend()\n", "plt.ylabel(\"Probability\", fontsize=16)\n", "plt.xlabel(\"cost\", fontsize=16)\n", "plt.tick_params(axis=\"both\", labelsize=14)" @@ -493,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { "tags": [] @@ -502,10 +538,10 @@ { "data": { "text/plain": [ - "{'x_0': 0, 'x_1': 0, 'x_2': 1}" + "{'x': [0, 0, 1]}" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -533,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { "pycharm": { diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod index 382226df1..1b153d454 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod @@ -1,7 +1,5 @@ qstruct QAOAVars { - x_0: qnum<2, False, 0>; - x_1: qnum<2, False, 0>; - x_2: qnum<2, False, 0>; + x: qnum<2, False, 0>[3]; monotone_rule_1_slack_var_0: qbit; monotone_rule_2_slack_var_0: qbit; } @@ -12,7 +10,7 @@ qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 3) { - phase (-(((((((((10 * v.x_0) * v.x_1) + ((10 * v.x_0) * v.x_2)) - v.x_0) + ((10 * v.x_1) * v.x_2)) - (2 * v.x_1)) - (3 * v.x_2)) + (10 * (((((v.monotone_rule_1_slack_var_0 + v.x_0) + v.x_1) + v.x_2) - 1.0) ** 2))) + (10 * (((((v.monotone_rule_2_slack_var_0 + v.x_0) + v.x_1) + v.x_2) - 1.0) ** 2))), params[i]); + phase (-(((((((((10 * v.x[0]) * v.x[1]) + ((10 * v.x[0]) * v.x[2])) - v.x[0]) + ((10 * v.x[1]) * v.x[2])) - (2 * v.x[1])) - (3 * v.x[2])) + (10 * (((((v.monotone_rule_1_slack_var_0 + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))) + (10 * (((((v.monotone_rule_2_slack_var_0 + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))), params[i]); apply_to_all(lambda(q) { RX(params[3 + i], q); }, v); diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json index af12aaf3f..0ae05c835 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ + "sx", "tdg", - "ry", + "z", "rz", - "s", - "r", - "sx", - "t", - "cy", - "p", - "u", + "y", "h", - "cx", + "cy", + "t", "sdg", - "z", - "x", - "cz", "u1", - "u2", + "r", + "id", + "cx", + "cz", "rx", + "u", + "p", + "ry", "sxdg", - "y", - "id" + "x", + "u2", + "s" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 2268737765 + "random_seed": 2943844644 } } diff --git a/applications/optimization/max_clique/max_clique.ipynb b/applications/optimization/max_clique/max_clique.ipynb index d8af81f07..b84d89a14 100644 --- a/applications/optimization/max_clique/max_clique.ipynb +++ b/applications/optimization/max_clique/max_clique.ipynb @@ -126,7 +126,7 @@ "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the QAOA algorithm [[1](#QAOA)], with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`." + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` python class. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm [[1](#QAOA)], with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`." ] }, { @@ -141,7 +141,7 @@ "from classiq import *\n", "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "combi = CombinatorialProblem(pyo_model=max_clique_model, num_layers=20)\n", + "combi = CombinatorialProblem(pyo_model=max_clique_model, num_layers=3)\n", "\n", "qmod = combi.get_model()" ] @@ -168,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", "metadata": { "pycharm": { @@ -176,7 +176,15 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/12b0d353-e44e-4992-bcb8-deb3a88a482b?version=0.62.0.dev7\n" + ] + } + ], "source": [ "qprog = combi.get_qprog()\n", "show(qprog)" @@ -219,9 +227,56 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Optimization Progress: 51it [02:27, 2.88s/it] \n" + ] + } + ], + "source": [ + "optimized_params = combi.optimize(execution_preferences, maxiter=50, quantile=0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b52604ae-fc57-426f-8566-9e3760250e81", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Cost convergence')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "optimized_params = combi.optimize(execution_preferences, maxiter=1, quantile=1)" + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" ] }, { @@ -239,7 +294,7 @@ "id": "2510a439-9181-4e39-a033-0bd53e8f87f6", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the pyomo to qmod translator." + "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the Pyomo to qmod translator." ] }, { @@ -247,12 +302,12 @@ "id": "e06ae35f-7aac-4631-9fe9-4f46a36c8cea", "metadata": {}, "source": [ - "In order to get samples with the optimized parameters, we call the `get_results` method:" + "In order to get samples with the optimized parameters, we call the `sample` method:" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "9638f749-a60b-4176-a4ea-50d7c2bb986f", "metadata": {}, "outputs": [ @@ -284,33 +339,33 @@ " \n", " \n", " \n", - " 99\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'...\n", - " 0.000977\n", + " 93\n", + " {'x': [0, 1, 1, 1, 0, 1, 0]}\n", + " 0.000488\n", " -4.0\n", " \n", " \n", - " 102\n", - " {'x_0': 1, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'...\n", - " 0.000977\n", + " 86\n", + " {'x': [1, 1, 1, 1, 0, 0, 0]}\n", + " 0.000488\n", " -4.0\n", " \n", " \n", - " 17\n", - " {'x_0': 1, 'x_1': 0, 'x_2': 0, 'x_3': 1, 'x_4'...\n", - " 0.015137\n", + " 50\n", + " {'x': [0, 1, 1, 0, 0, 1, 0]}\n", + " 0.003418\n", " -3.0\n", " \n", " \n", - " 26\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", - " 0.012695\n", + " 41\n", + " {'x': [1, 1, 0, 1, 0, 0, 0]}\n", + " 0.004883\n", " -3.0\n", " \n", " \n", - " 27\n", - " {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", - " 0.012695\n", + " 44\n", + " {'x': [1, 0, 1, 1, 0, 0, 0]}\n", + " 0.004395\n", " -3.0\n", " \n", " \n", @@ -318,58 +373,40 @@ "" ], "text/plain": [ - " solution probability cost\n", - "99 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'... 0.000977 -4.0\n", - "102 {'x_0': 1, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4'... 0.000977 -4.0\n", - "17 {'x_0': 1, 'x_1': 0, 'x_2': 0, 'x_3': 1, 'x_4'... 0.015137 -3.0\n", - "26 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.012695 -3.0\n", - "27 {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.012695 -3.0" + " solution probability cost\n", + "93 {'x': [0, 1, 1, 1, 0, 1, 0]} 0.000488 -4.0\n", + "86 {'x': [1, 1, 1, 1, 0, 0, 0]} 0.000488 -4.0\n", + "50 {'x': [0, 1, 1, 0, 0, 1, 0]} 0.003418 -3.0\n", + "41 {'x': [1, 1, 0, 1, 0, 0, 0]} 0.004883 -3.0\n", + "44 {'x': [1, 0, 1, 1, 0, 0, 0]} 0.004395 -3.0" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "optimization_result = combi.get_results()\n", + "optimization_result = combi.sample(combi.optimized_params)\n", "optimization_result.sort_values(by=\"cost\").head(5)" ] }, + { + "cell_type": "markdown", + "id": "d2631766-717e-41d1-889d-a2c83ff479e3", + "metadata": {}, + "source": [ + "We will also want to compare the optimized results to uniformly sampled results:" + ] + }, { "cell_type": "code", - "execution_count": 9, - "id": "9b868135-e219-441c-8d98-6b43d894a130", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 10, + "id": "ce23d35b-4780-4feb-a1fa-58e302e3feb3", + "metadata": {}, + "outputs": [], "source": [ - "solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", - "solution_nodes = [v for v in graph.nodes if solution[f\"x_{v}\"]]\n", - "solution_edges = [\n", - " (u, v) for u, v in graph.edges if u in solution_nodes and v in solution_nodes\n", - "]\n", - "nx.draw_kamada_kawai(graph, with_labels=True)\n", - "nx.draw_kamada_kawai(\n", - " graph,\n", - " with_labels=True,\n", - " nodelist=solution_nodes,\n", - " edgelist=solution_edges,\n", - " node_color=\"r\",\n", - " edge_color=\"r\",\n", - ")" + "uniform_result = combi.sample_uniform()" ] }, { @@ -377,12 +414,12 @@ "id": "687f492b-a4a5-49c6-964c-8959b035bb93", "metadata": {}, "source": [ - "And the histogram:" + "And compare the histograms:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8", "metadata": { "tags": [] @@ -390,7 +427,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -400,11 +437,23 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", "optimization_result[\"cost\"].plot(\n", - " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", + ")\n", + "plt.legend()\n", "plt.ylabel(\"Probability\", fontsize=16)\n", "plt.xlabel(\"cost\", fontsize=16)\n", "plt.tick_params(axis=\"both\", labelsize=14)" @@ -420,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { "tags": [] @@ -429,10 +478,10 @@ { "data": { "text/plain": [ - "{'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 1, 'x_4': 0, 'x_5': 1, 'x_6': 0}" + "{'x': [1, 1, 1, 1, 0, 0, 0]}" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -442,6 +491,41 @@ "best_solution" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9b868135-e219-441c-8d98-6b43d894a130", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "solution_nodes = [v for v in graph.nodes if best_solution[\"x\"][v]]\n", + "solution_edges = [\n", + " (u, v) for u, v in graph.edges if u in solution_nodes and v in solution_nodes\n", + "]\n", + "nx.draw_kamada_kawai(graph, with_labels=True)\n", + "nx.draw_kamada_kawai(\n", + " graph,\n", + " with_labels=True,\n", + " nodelist=solution_nodes,\n", + " edgelist=solution_edges,\n", + " node_color=\"r\",\n", + " edge_color=\"r\",\n", + ")" + ] + }, { "cell_type": "markdown", "id": "149932e1-bfa8-4c27-b5f9-037e74eba400", @@ -460,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { "pycharm": { @@ -490,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "79666d4d-e105-4706-b44e-e5c73b928af3", "metadata": { "tags": [] @@ -534,7 +618,6 @@ "tags": [] }, "source": [ - "\n", "## References\n", "\n", "[1]: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n", diff --git a/applications/optimization/max_clique/max_clique.qmod b/applications/optimization/max_clique/max_clique.qmod index f2779147b..bceade30c 100644 --- a/applications/optimization/max_clique/max_clique.qmod +++ b/applications/optimization/max_clique/max_clique.qmod @@ -1,22 +1,16 @@ qstruct QAOAVars { - x_0: qbit; - x_1: qbit; - x_2: qbit; - x_3: qbit; - x_4: qbit; - x_5: qbit; - x_6: qbit; + x: qbit[7]; } -qfunc main(params: real[40], output v: QAOAVars) { +qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); - repeat (i: 20) { - phase (-((((((((-v.x_0) - v.x_1) - v.x_2) - v.x_3) - v.x_4) - v.x_5) - v.x_6) + (2 * ((((((((2 * v.x_0) * v.x_5) + (v.x_1 * v.x_4)) + (v.x_2 * (v.x_4 + v.x_6))) + (v.x_3 * v.x_6)) + (v.x_4 * ((v.x_1 + v.x_2) + v.x_6))) + (v.x_6 * ((v.x_2 + v.x_3) + v.x_4))) ** 2))), params[i]); + repeat (i: 3) { + phase (-((((((((-v.x[0]) - v.x[1]) - v.x[2]) - v.x[3]) - v.x[4]) - v.x[5]) - v.x[6]) + (2 * ((((((((2 * v.x[0]) * v.x[5]) + (v.x[1] * v.x[4])) + (v.x[2] * (v.x[4] + v.x[6]))) + (v.x[3] * v.x[6])) + (v.x[4] * ((v.x[1] + v.x[2]) + v.x[6]))) + (v.x[6] * ((v.x[2] + v.x[3]) + v.x[4]))) ** 2))), params[i]); apply_to_all(lambda(q) { - RX(params[20 + i], q); + RX(params[3 + i], q); }, v); } } diff --git a/applications/optimization/max_clique/max_clique.synthesis_options.json b/applications/optimization/max_clique/max_clique.synthesis_options.json index bcb7020f0..bbb495713 100644 --- a/applications/optimization/max_clique/max_clique.synthesis_options.json +++ b/applications/optimization/max_clique/max_clique.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ + "r", + "id", + "u", + "cx", "sx", + "h", "y", - "cy", - "t", - "sdg", - "p", - "cz", + "u1", "s", - "id", - "u", - "r", - "rz", + "u2", "ry", + "rz", + "p", + "t", + "cz", "rx", - "u2", - "u1", - "cx", + "sdg", + "cy", + "x", "tdg", "sxdg", - "h", - "z", - "x" + "z" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 264602322 + "random_seed": 4176492311 } } diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb index a299238ad..6ec67e78c 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.ipynb @@ -42,7 +42,7 @@ "\n", "# Solving with the Classiq platform\n", "\n", - "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve." + "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a Pyomo model for the optimization problem we would like to solve." ] }, { @@ -68,7 +68,7 @@ "source": [ "## Building the Pyomo model from a graph input\n", "\n", - "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" + "We proceed by defining the Pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" ] }, { @@ -193,7 +193,7 @@ "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` python class. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm, with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "69d7c8ac-29c6-42b7-9111-fcf88a4e816a", "metadata": { "pycharm": { @@ -248,7 +248,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/9e9127b4-7c66-4b81-b66c-57745580a2ec?version=0.61.0.dev8\n" + "Opening: https://nightly.platform.classiq.io/circuit/6cb47285-352a-47be-8371-154272108ce1?version=0.62.0.dev9\n" ] } ], @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "c052e252-745c-4b93-8df7-992d3c5d3277", "metadata": {}, "outputs": [], @@ -289,17 +289,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 10, "id": "066869ce-6c80-4e8a-9943-77ceabe74388", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Optimization Progress: 77%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 69/90 [04:58<01:30, 4.32s/it]\n" + ] + } + ], "source": [ - "cost_values = []\n", - "optimized_params = combi.optimize(\n", - " execution_preferences, maxiter=90, cost_trace=cost_values, quantile=0.7\n", - ")" + "optimized_params = combi.optimize(execution_preferences, maxiter=90, quantile=0.7)" ] }, { @@ -312,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "id": "be6bc7ec-c1f8-4925-be2b-0dceb20d73cc", "metadata": { "tags": [] @@ -324,13 +329,13 @@ "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 23, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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n8Mwzz6CwsBAnT57EXXfdhTNnzuD9998P5mUTERFRCAm5CcDJycn4xz/+gdtuu63dfStWrMCvfvUrNDY2QqXyLA6rq6uDTqdDbW0ta2aIiIjChDef3yFTAGw0GrFixQo0NjZi7NixTh8j/UCeBjJERETU/QU9KtizZw/Gjh2LlpYWxMXFYeXKlSgsLGz3uKqqKjz++OO44447Onw+vV4PvV4v/7murs7v10xEREShI+jdTP3798fu3buxdetW3H333ZgzZw5++uknu8fU1dXhqquuQmFhIR599NEOn2/hwoXQ6XTyFwfmERERdW8hVzMzefJk9O7dG//+978BAPX19Zg6dSpiYmKwevVqREVFdfj9zjIzeXl5rJkhIiIKI2FZMyMxmUxyMFJXV4epU6dCq9Vi1apVbgMZANBqtdBqtYG+TCIiIgoRQQ1mFixYgGnTpqFHjx6or6/HsmXLsGnTJnzxxReoq6vDlClT0NTUhLfffht1dXVy/UtaWhqUSmUwL52IiIhCRFCDmYqKCtx88804e/YsdDodhg4dii+++AJXXHEFNm3ahK1btwIA+vTpY/d9x48fR35+fhCumIiIiEJNyNXM+BvnzBAREYUfbz6/g97NRJ3T3GpEN49HiYiIOsRgJowdq2zABf/3JR775Cf3DyYiIuqmGMyEsf1n66FvM2HXqZpgXwoREVHQMJgJY/o2IwCgtc0U5CshIiIKHgYzYUwKYgxGBjNERBS5GMyEsVZLEMPMDBERRTIGM2FMb2AwQ0RExGAmjMmZGR4zERFRBGMwE8b0Us0MMzNERBTBGMyEMambSc/MDBERRTAGM2FMqpVpbTNxCjAREUUsBjNhTG9zvGQwMpghIqLIxGAmjNl2MbEImIiIIhWDmTBmG8ywCJiIiCIVg5kwJhUAA8zMEBFR5GIwE8bsjpmYmSEiogjFYCaM2WZj9AxmiIgoQjGYCWPSOgOAyyaJiChyMZgJY7aZGR4zERFRpGIwE8ZsMzMsACYiokjFYCaMMTNDRETEYCascWgeERERg5mwZjdnhpkZIiKKUAxmwpiec2aIiIgYzIQzDs0jIiJiMBO2RFF02JrNYIaIiCITg5kwZTCKdn9mATAREUUqBjNhyjF44TETERFFKgYzYUpvMNr/mcEMERFFKAYzYcoxM8OaGSIiilQMZsKU7SoDgMdMREQUuRjMhCnWzBAREZkxmAlTjsELu5mIiChSBTWYWbx4MYYOHYqEhAQkJCRg7Nix+Pzzz+X7W1paUFRUhJSUFMTFxeG6665DeXl5EK84dNiuMgBYM0NERJErqMFMbm4unnrqKRQXF2PHjh24/PLLMWPGDOzbtw8AcP/99+OTTz7BihUrsHnzZpw5cwazZs0K5iWHDMfuJXYzERFRpFIF88WvueYauz8/8cQTWLx4MbZs2YLc3Fy88cYbWLZsGS6//HIAwJIlSzBw4EBs2bIFY8aMCcYlh4x2x0wMZoiIKEKFTM2M0WjE8uXL0djYiLFjx6K4uBgGgwGTJ0+WHzNgwAD06NED33//vcvn0ev1qKurs/vqjhwzMQxmiIgoUgU9mNmzZw/i4uKg1Wpx1113YeXKlSgsLERZWRk0Gg0SExPtHp+RkYGysjKXz7dw4ULodDr5Ky8vL8A/QXA4Bi+smSEiokgV9GCmf//+2L17N7Zu3Yq7774bc+bMwU8//eTz8y1YsAC1tbXy16lTp/x4taGD3UxERERmQa2ZAQCNRoM+ffoAAEaOHInt27fjhRdewOzZs9Ha2oqamhq77Ex5eTkyMzNdPp9Wq4VWqw30ZQcdj5mIiIjMgp6ZcWQymaDX6zFy5Eio1WqsX79evu/gwYMoKSnB2LFjg3iFoaHV0podrVZa/sxghoiIIlNQMzMLFizAtGnT0KNHD9TX12PZsmXYtGkTvvjiC+h0Otx222144IEHkJycjISEBNxzzz0YO3ZsxHcyAdbMTFyUCs0GI1qNYpCviIiIKDiCGsxUVFTg5ptvxtmzZ6HT6TB06FB88cUXuOKKKwAA//znP6FQKHDddddBr9dj6tSpePnll4N5ySFDysTEa1WorNfLmRoiIqJIE9Rg5o033ujw/qioKCxatAiLFi3qoisKH1LBb1yUyu7PREREkSbkambIM/Ixk9YSzLBmhoiIIhSDmTDV6hDMGFgzQ0REEYrBTJiSMjPxUWoAzMwQEVHkYjATpqSt2fFRPGYiIqLIxmAmTDkeM7UaTRBFHjUREVHkYTATplrb7LuZANbNEBFRZGIwE6Ycu5kAtmcTEVFkYjATpuSheTaZGdbNEBFRJGIwE6akAmCtSgmlQgDAYIaIiCITg5kwJR0padUKaJTm/4wGHjMREVEEYjATpqQsjFapgEZl/s+oZ2aGiIgiEIOZMCUFLhqVNZjhMRMREUUiBjNhSs7MqJTyMRO7mYiIKBIxmAlTrU4yM6yZISKiSMRgJkzp5cyMtQCYx0xERBSJGMyEKWeZGQYzREQUiRjMhCFRFOX6GNtght1MREQUiRjMhCHboEWrUkCtNA/NY80MERFFIgYzYci2a8mcmVGab2dmhoiIIhCDmTCkN9gEM0oFW7OJiCiiMZgJQ7b1MoIgQMsCYCIiimAMZsKQ7SoDAKyZISKiiMZgJgzJG7PV5v987GYiIqJIxmAmDMkzZpT2wQyPmYiIKBIxmAlDtgPzAECjtHQz8ZiJiIgiEIOZMKS3WTIJAGqVpWaGmRkiIopADGbCkGNmRsvWbCIiimAMZsKQ3vGYiTUzREQUwRjMhCG5m4nBDBEREYOZcOR4zKS2HDPpecxEREQRiMFMGNK7aM1mATAREUUiBjNhSJ4ArDZ3M3E3ExERRbKgBjMLFy7EqFGjEB8fj/T0dMycORMHDx60e0xZWRl+/etfIzMzE7GxsRgxYgQ++OCDIF1xaJB3M3FoHhERUXCDmc2bN6OoqAhbtmzB2rVrYTAYMGXKFDQ2NsqPufnmm3Hw4EGsWrUKe/bswaxZs3DDDTdg165dQbzy4JK2ZsvrDJQMZoiIKHKpgvnia9assfvz0qVLkZ6ejuLiYlx66aUAgO+++w6LFy/GRRddBAD4y1/+gn/+858oLi7G8OHDu/yaQ0Gr0dzN1K5mhsdMREQUgUKqZqa2thYAkJycLN82btw4vPvuu6iurobJZMLy5cvR0tKCiRMnOn0OvV6Puro6u6/uRq6ZcWjN5qJJIiKKRCETzJhMJsyfPx/jx4/H4MGD5dvfe+89GAwGpKSkQKvV4s4778TKlSvRp08fp8+zcOFC6HQ6+SsvL6+rfoQuo3cMZlgATEREESxkgpmioiLs3bsXy5cvt7v94YcfRk1NDdatW4cdO3bggQcewA033IA9e/Y4fZ4FCxagtrZW/jp16lRXXH6XajdnhgXAREQUwYJaMyOZN28eVq9eja+++gq5ubny7UePHsW//vUv7N27F4MGDQIADBs2DF9//TUWLVqEV155pd1zabVaaLXaLrv2YGi/NZs1M0REFLmCGsyIooh77rkHK1euxKZNm1BQUGB3f1NTEwBAobBPICmVSphMkfvB7bg1W8vMDBERRbCgBjNFRUVYtmwZPv74Y8THx6OsrAwAoNPpEB0djQEDBqBPnz6488478cwzzyAlJQUfffQR1q5di9WrVwfz0oOKiyaJiIisglozs3jxYtTW1mLixInIysqSv959910AgFqtxmeffYa0tDRcc801GDp0KP7zn//grbfewvTp04N56UElLZqUjpfULAAmIqIIFvRjJnf69u0b8RN/HVnXGTjOmREhiiIEQQjatREREXW1kOlmIs+5Wmdgex8REVGkYDAThqzrDOwXTQKsmyEiosjDYCYMtcvMMJghIqIIxmAmDDnOmVEoBKgU5joZg9F9HRIREVF3wmAmDEndTFqbWhm2ZxMRUaRiMBOGHBdNAjbBjGWjNhERUaRgMBOGHI+ZAOusGW7OJiKiSMNgJgw5rjMAbPczsWaGiIgiC4OZMGM0iWgzmQMW28wM9zMREVGkYjATZmyDFRYAExERMZgJO7bBirOaGRYAExFRpGEwE2b0lmBFECDPlgFsMzOsmSEiosjCYCbMyKsMVAq7hZIabs4mIqIIxWAmzDiuMpCwZoaIiCIVg5kwY50xo7S7Xa6ZYTBDREQRhsFMmNE7mf5r+2cDj5mIiCjCMJgJM85WGQA8ZiIiosjFYCbMOFtlALAAmIiIIheDmTDjbGM2AKhVguV+BjNERBRZGMyEGdeZGXNBMGtmiIgo0jCYCTPOlkwCrJkhIqLIxWAmzLjMzDCYISKiCMVgJszoXQ3NU5prZhjMEBFRpGEwE2b0BksBsNp5ZoY1M0REFGkYzIQZl+sMLH/WM5ghIqIIw2AmzLiumVHa3U9ERBQpGMyEGVfdTGrWzBARUYRiMBNm3HUzsWaGiIgiDYOZMCNNAHYMZrRszSYiogjFYCbMuF00ycwMERFFGAYzYcZVMKNWMjNDRESRicFMmNG7ysxwazYREUWooAYzCxcuxKhRoxAfH4/09HTMnDkTBw8ebPe477//HpdffjliY2ORkJCASy+9FM3NzUG44uDjOgMiIiJ7QQ1mNm/ejKKiImzZsgVr166FwWDAlClT0NjYKD/m+++/x5VXXokpU6Zg27Zt2L59O+bNmweFIjKTSvLQPAYzREREAABVMF98zZo1dn9eunQp0tPTUVxcjEsvvRQAcP/99+Pee+/FQw89JD+uf//+XXqdoURvcLE1m8dMREQUoUIqvVFbWwsASE5OBgBUVFRg69atSE9Px7hx45CRkYEJEybgm2++cfkcer0edXV1dl/dictFk9KcGWZmiIgowoRMMGMymTB//nyMHz8egwcPBgAcO3YMAPDoo4/iN7/5DdasWYMRI0Zg0qRJOHz4sNPnWbhwIXQ6nfyVl5fXZT9DV3BbM8PMDBERRZiQCWaKioqwd+9eLF++XL7NZDJ/MN9555245ZZbMHz4cPzzn/9E//798eabbzp9ngULFqC2tlb+OnXqVJdcf1eRhua56mYyGEWYTGKXXxcREVGwBLVmRjJv3jysXr0aX331FXJzc+Xbs7KyAACFhYV2jx84cCBKSkqcPpdWq4VWqw3cxQaZq8yM2ubPrUYTohT2NTVERETdVVAzM6IoYt68eVi5ciU2bNiAgoICu/vz8/ORnZ3drl370KFD6NmzZ1deashwtWjStoaG+5mIiCiSBDUzU1RUhGXLluHjjz9GfHw8ysrKAAA6nQ7R0dEQBAEPPvggHnnkEQwbNgwXXHAB3nrrLRw4cADvv/9+MC89aFzWzNgEM2zPJiKiSBLUYGbx4sUAgIkTJ9rdvmTJEsydOxcAMH/+fLS0tOD+++9HdXU1hg0bhrVr16J3795dfLWhwdU6A4VCgFopwGAUWQRMREQRJajBjCh6Vqj60EMP2c2ZiWSuCoAB834mg9HIzAwREUWUkOlmIvfajCZIjUqOx0y2t4VKzczp8014dNU+lJxrCvalEBFRN8ZgJozYHh85DWYsdTP6EMnM/Of7k1j63Qn8d8uJYF8KERF1Ywxmwoi0ygBoPwEYCL39TCfPmXdsnWtsDfKVEBFRd8ZgJoxImRmlQoDKWTCjDK1g5vR582bzuua2IF8JERF1Zwxmwojclu0kkAFsa2ZCYwKwHMy0GIJ8JURE1J0xmAkjcieTuuNgptVo7LJrcqWuxYDaZnMQU9fMYIaIiAKHwUwY0bvLzITQMVOpJSsDAPUtPGYiIqLAYTATRuRVBi4yM+oQ6mY6bRPMMDNDRESBxGAmjHhaMxMKmZnT562zZer1bTBykzcREQUIg5kwYt3L5HwjdigVANtmZgCggUdNREQUIAxmwojexV4miTUzE/wCYNvMDMCOJiIiChwGM2HE1cZsiVwAHALrDBwzM7WsmyEiogBhMBNGpJZrl5mZEOpmkoIZlUIAwMwMEREFDoOZMCKtM3B7zBTkmhnbGTO90+LMt3EKMBERBQiDmTAiHR+5PGYKkW4macZMUowaWYlRAJiZISKiwGEwE0bctWarA3zMZPKwvVo6YspNikFClBoAZ80QEVHgMJgJI9Zupo5bswOxzuDJz/ZjxN/W4lR1k9vHSp1MuUnRSIhWAQDq2JpNREQBwmAmjOjddDNJtTSGNv/XzKzbX46aJgO+Plzl9rHWzEw0MzNERBRwqmBfAHlOXjQZhNbsqno9AOBAWZ3bx1ozMzFoNpivmTUzREQUKMzMhBF3c2bUSsHucf6ibzPKx0QHyurdPt55ZobHTEREFBjMzIQRt0PzLLU0/l40ea6hVf7fB8vqIYoiBEFw+XjbAmBmZoiIKNCYmQkjnhYAG/x8zFRpOWICzJN8y+paXD7WdsZMDmtmiIioCzCYCSPuMzOBac2uatDb/bmjoybbGTNxWhUSos3BTH0IdjOdb2zFjhPVwb4MIiLqJAYzYcT9biZLzYyfMzPtgpmzroMZ2yMmAEiIsrRmh2Bm5i8f7cXPX/keXx+uDPalEBFRJzCYCSNuu5kClplptfvzwQ46mmxnzACwZmb0bTB6OHSvq2yzZGW+O3ouyFdCRESdwWAmjEgZF9et2eZamkDVzPTLMO9Z6uiYybaTCQDio6w15g0hdNR0vrFV/rn2ltYG+WqIiKgzGMyEkdY2DxdNBqhm5uI+aQCAo5UNLgMm2xkz5mtVIkptvq5Q6mg6VG4NyPaU1kIUQytrREREnvMpmPm///s/NDW1H2vf3NyM//u//+v0RZFz7iYAS3Nm/N2aLWUwhuXpEKdVwWAUcayy0eljHTMzAOSOptoQqpuxDWZqmgworWkO4tUQEVFn+BTMPPbYY2hoaGh3e1NTEx577LFOXxQ5Z1006W43U2AyM2nxWvTPjAfgehKwYwEwYK2bCaXMzMFy+6MyHjUREYUvn4IZV0PTfvjhByQnJ3f6osg5ec6M2s1uJr8HM+YC4LQ422Cmfd2M44wZibWjKXRqZg6VmYNxqaZnD4MZIqKw5dUE4KSkJAiCAEEQ0K9fP7uAxmg0oqGhAXfddZffL5LMrJmZjguA/Vkz09pmkgOU1DgtBlqCmYNOghnHGTOSUMvMiKIoZ2auHpqFd7adwt5S9zuniIgoNHkVzDz//PMQRRG33norHnvsMeh0Ovk+jUaD/Px8jB071u8XSWbuamYCUQB8rtF8xKRSCNBFq9E/MwEAcOBs+w9/Z0dMAEJuCnBlvR61zQYoBGDGBTmWYKbW7ZoGIiIKTV4FM3PmzAEAFBQUYPz48VCpOrfaaeHChfjwww9x4MABREdHY9y4cfj73/+O/v37t3usKIqYPn061qxZg5UrV2LmzJmdeu1w5G7OjFQA3GYSYTKJUCg6/8FcVW8+YkqJ00ChEORjpjO1LahtNkBnyboA7WfMSBKiLcdMIdKaLWVl8lNjcUFeIpQKAecaW3G2tgXZidFuvpuIiEKNTzUz8fHx2L9/v/znjz/+GDNnzsSf/vQntLa2dvCd9jZv3oyioiJs2bIFa9euhcFgwJQpU9DY2L5T5vnnn4/4fzV7us4A8F8RcGWDeQ9TapwWAKCLViNbFwXAviMIcN7JBIReZkY6IuufEY8otRJ9083zc1gETEQUnnwKZu68804cOnQIAHDs2DHMnj0bMTExWLFiBf7whz94/Dxr1qzB3LlzMWjQIAwbNgxLly5FSUkJiouL7R63e/duPPvss3jzzTd9udxuQRRFOUDpymBGysykxWvl2+QiYIejJscZM5JQq5mRgrC+GeafY3CO+biUwQwRUXjyKZg5dOgQLrjgAgDAihUrMGHCBCxbtgxLly7FBx984PPF1NaaP0xsO6Kamprwy1/+EosWLUJmZqbb59Dr9airq7P76g4MRhHSXDeXW7NtCoP9VTdTaWnLljIzADAgy1I3U+ZtZiZUjpnMnUz9LcHMEEsww44mIqLw5HNrtslk/rBct24dpk+fDgDIy8tDVVWVTxdiMpkwf/58jB8/HoMHD5Zvv//++zFu3DjMmDHDo+dZuHAhdDqd/JWXl+fT9YQa20yLq5oZQRDkuhl/BTNVzoIZF+3ZLguA5ZqZ4GdmTCYRRyyZmf6Z5uOlwXIwU8dJwEREYcinYObCCy/E3/72N/z3v//F5s2bcdVVVwEAjh8/joyMDJ8upKioCHv37sXy5cvl21atWoUNGzbg+eef9/h5FixYgNraWvnr1KlTPl1PqLENTly1Ztve569ZM9KMmdQ4jXzbAEtH06GyevnD39WMGSC0amZKa5rR2GqERqlAz5RYAEBhVgIUgjlwq6jXu3kGIiIKNT4FM88//zx27tyJefPm4c9//jP69OkDAHj//fcxbtw4r59v3rx5WL16NTZu3Ijc3Fz59g0bNuDo0aNITEyESqWSu6euu+46TJw40elzabVaJCQk2H11B1Ink1opdNil5O/27Kp66/RfSa+0WKiVAur1bfIaAFczZgCbzdkh0M0k1cuYfwbzexWtUaKPpQh4z2keNRERhRufequHDh2KPXv2tLv9H//4B5QuRu07I4oi7rnnHqxcuRKbNm1CQUGB3f0PPfQQbr/9drvbhgwZgn/+85+45pprfLn0sOVuYJ5ECmb8tZ/JWc2MWqlA77Q4HCirx4Gz9chNinF5xATYTgAOfmbmoHzEFG93++AcHQ6VN2BPaS0mF/qWXSQiouDo1KCY4uJiuUW7sLAQI0aM8Or7i4qKsGzZMnz88ceIj49HWVkZAECn0yE6OhqZmZlOi3579OjRLvDp7qyrDDoOFqVsg9+6mRraZ2YAc93MgbJ6HCyvx+TCDJczZgCbzIy+DUaTCKUf5t/46pClzqdfhkMwk63DhztLse8MMzNEROHGp2CmoqICs2fPxubNm5GYmAgAqKmpwWWXXYbly5cjLS3No+dZvHgxALQ7MlqyZAnmzp3ry6V1W95mZgx+yMwYjCbUNFlXGdgyTwI+g/2W9mxXnUyAtWYGABpa2qCLUbd7TFeROpkcg5khuexoIiIKVz7VzNxzzz1oaGjAvn37UF1djerqauzduxd1dXW49957PX4eURSdfnUUyIiiGKHTfzueMSPR+DEzc85S/KtUCEiMtg9ABmTZ72hyNWMGMF9ztCWjFMyOpjajCUcr7duyJYVZCRAEoLxOj4r6lmBcHhER+cinYGbNmjV4+eWXMXDgQPm2wsJCLFq0CJ9//rnfLo6s3K0ykGj9WAAsHTGlxGraFR1L7dnHqhqhbzN2mJkBrO3ZtUGsmzlZ3YTWNhOi1cp21xmrVaF3mrkIeB+XThIRhRWfghmTyQS1uv1RgVqtlufPkH+5W2UgkWtm/BDMOCv+lWQmRCEhSgWjScSRioYOC4ABm/bsIGZmrPUycU47wgZnmzvfeNRERBRefApmLr/8ctx33304c+aMfFtpaSnuv/9+TJo0yW8XR1aeBjNya7YfjpkqLW3ZqfHtgxlBEORJwDtOnHc5Y0YirzQI4hTggw5rDBwN5iRgIqKw5FMw869//Qt1dXXIz89H79690bt3bxQUFKCurg4vvfSSv6+RYNPN5Gkw48djJtuBebako6Z1+8sBOJ8xI5Hbs4OZmSm3Lph0RlprsI/BDBFRWPGpmykvLw87d+7EunXrcODAAQDAwIEDMXnyZL9eHFlZMzMdt2b7swDY2ZJJW9Ik4K3HqgG4PmICbDMzwQxmLJ1Mmc6DmULLMdOZ2haca9AjxcnxGgVGa5sJCz/fjwn90jCxf3qwL4eIwoxXmZkNGzagsLAQdXV1EAQBV1xxBe655x7cc889GDVqFAYNGoSvv/46UNca0aTgxF1mRh2AzEyaiw91afCcdG2uin8B25qZ4Bwz6duMOF7VCMB1ZiY+So1eqeYVBzxq6lrfHq3Ckm9P4O9rDgb7UogoDHkVzDz//PP4zW9+43RFgE6nw5133onnnnvObxdHVnqDuZvJXc2M1o+7mZwtmbTlOEW3w2AmOrhTgI9VNsJoEpEQpUJGguuMi1Q3s+8MO5q60hnLWozyOrbFE5H3vApmfvjhB1x55ZUu758yZQqKi4s7fVHUnpyZ8XBonn9rZpx/+MdpVchLtgYwHR4zBbmbSaqX6ZcRD0FwPYFYqpvhjqauVV5rDmKqG1v9tleMiCKHV8FMeXm505ZsiUqlQmVlZacvitrTG6R1Bl0XzFi7mZwXAANA/wxrlq7jzExwu5mk4X6u6mUkg3I6bs9uam3DxgMVaLFkysg/yuus28rPNXJzORF5x6tgJicnB3v37nV5/48//oisrKxOXxS1J2Vm3K0zkObM6Dt5zGQwmnDexSoDWwOzrMFBaGdmnE/+dSQdM5XWNON8Y6vdfTtOVGPaC1/jlqXb8fjqnwJzoRGqzOZ4qaKOwQwRecerYGb69Ol4+OGH0dLS/ly7ubkZjzzyCK6++mq/XRxZeTtnxtAmdur1qhutqwySYjrIzNhkOlzNmAGCXzNje8zUkYQoNfJTzEHZXsvSyRaDEQs/34/r//09Tp4zr21YtftM0LIzRpOIbw5Xyf+NugPbWhkpI0hE5CmvWrP/8pe/4MMPP0S/fv0wb9489O/fHwBw4MABLFq0CEajEX/+858DcqGRzjpnxtPW7M590EofKMmxmg63XA/NSYQgANm6aJczZgBrZqY+CN1MTa1tKKk2ByH9MuLcPn5Qjg4nzjVhT2ktEqM1+N2K3XJm57oRudhy7BxKa5qx4UAFpg/pukykKIrYdLASCz/fj0PlDZg8MB2vzxnVZa8fSHbBTAODGSLyjlfBTEZGBr777jvcfffdWLBgAUTR/K9/QRAwdepULFq0CBkZGQG50Ejn8aJJP9XMuCv+lfRIicFbt1yE9A46hIDgzpk5bAlEUuO0Hs2OGZKjw6c/nsV/vz+J5748hDaTiNQ4DZ68dgimDMrE39ccwOJNR/HRrtIuC2Z+PF2DhZ8dwPfHzsm37Sqp6ZLXDjR9m1E+0gR4zERE3vN6aF7Pnj3x2Wef4fz58zhy5AhEUUTfvn2RlJQUiOsjC4+Pmfy0m6nKsjHb1fRfW5f2S3P7GGkCcL2+DUaT2GG2x98Ollt3MnlC6mg6a+mwmT4kE3+bOQTJseb3YuYFOVi86Sg2HqxATVMrEjs4huusknNN+MeXB/HJD+bVIRqVAjeP6Yk3vj2Oc42tqGrQuw04Q51j8FLZwPZsIvKOTxOAASApKQmjRnWPFHc48HRrtlwzY+xczYx0zORqYJ634qOsXXANLW3QxbjuivM364LJjutlJENzddBZMkn/N2MQfjYs266du39mPAZkxuNAWT0+21OGX47u4f+LBvDGN8fx1Of7YTCKEATg2uE5+N2U/shJjMba/eU4ea4Jh8rqkdonvIMZx9kyzMwQkbd82s1EXc/bAmC9v46ZXKwy8JZGpUC02lzv09UdTYcqLJ1MbtqyJfFRamx+cCK+e+hyzLggx+lcmpnDcwAAH+0u9d+F2qhvMeDJz8yBzCV9U7H6novx3A0XICfRXGQtdWVJWadwVuYQzLBmhoi8xWAmTFjXGXTNbiZ3qwx8IXU01XZx3Yy3mRkASIzRILaDgmZztgbYdrwapZbptf5UfPI8jCYRecnR+O9tozEoW2d3vxSYSfNzwpk0Y0YK1JiZISJvMZgJE9LQPHeZGetups51M1kzM/6rBwnGrJnaJoP8L39Pa2Y8kZ0YjdEFyQDMbdr+tvW4eXnn6IIUp/f360aZGemYSapVqmzQy80FRESeYDATJjwdmqdR+qdmRtqY7c/i0mBMAT5UYf6wz9ZF2dXt+MPMC8xHTR8H4Khpq6VrSQqYHEmZmUNl9WH/wS8FM4Mt05db20xBmxRNROGJwUyYkAuA3awz0PqpNbvSw9Zsb0gdTV2ZmXl/x2kAQGF2++WonTVtSBY0SgUOlNVj/1n/LaZsam3Dj5bdUGN6Oc/MFKTGQq0U0NhqDMgxV1cqs3SN5SXHyL8j7GgiIm8wmAkTUnDSFYsm24wmnG8KZGama4KZHSeq8e6OUwCAuyb09vvz66LVuGyAuS3dn4XAO0/WoM0kIlsX5XLflVqpQO8087FZuNfNVFg65zITopBmKThn3QwReYPBTJjwtJtJ7YcC4OrGVogioBAgz1bxB2vNjOsjhJqmVlz14tdYtPFIp17LYDThzyvNe8R+MSoPF+Y7P67pLOmo6ZPdZ2Ay+ee4Z+txyxFTr5QON3x3h7oZURTlzExGQhTS46MAsKOJiLzDYCZMeLzOwA+ZGemDxN0qA295sp/pq8NV2HemDu9uP9Wp13rzm+M4WF6P5FgN/njlgE49V0cuG5CO+CgVztS2YNuJar8859ZjUvFvxwGYbd1MuKrXt6HZsuMqwyYzw/1MROQNBjNhwusJwJ3IzFin//p3GJsn3UxHLTNhyutafC5sPX2+Cc+vOwwAWDBtAJL8mF1yFKVWYtrgTAD+KQRuMRix+1QNAHNmpiPWWTMNnX7dYCm3ZGUSolSI1iiRLh0zMZghIi8wmAkTcs1MF+xmqpKm//ppYJ7Ek26mo5XmD2Z9JzpaHl31E5oNRlxUkIyfj8z16Tm8IQ3Q+/THs3Khtq92ldSg1WhCerxW3t7tipSZOVrRAEMn5woFizRjJlNnPl5iZoaIfMFgJkx4vGjSD7uZAtHJBHiYmalslP+342RYT3y5rwzr9pdDpRDwxMzBHdac+MuYghRkJkShrqUNGw9Uduq5PK2XAcxD5mI0SrQaTTh5rrHDx4Yq6b9xRoI5mJEWllbUs5uJiDzHYCYMiKJonTPjaWamM8dM9VIw49/jGXc1MyaTiGOV1iMTx5097jTq2/Doqn0AgDsu7YW+Xkz87QyFQsDPLsgG0PmjJk/rZaTXlYuAy8LzqKncIZhJi7MUADMzQ0ReYDATBmz3LHl6zGQ0iTD62F1TFeDMTL2LbqbSmma7n9XbYObF9YdxprYFuUnRuOfyvr5fqA9mWIKZ9QcqfF7XoG8zYmfJeQDAmF6edV/JdTNl/ptz05WswYz5dy2NNTNE5AMGM2HANsviaWYGgM91FAErAHYzZ+ZIpX12wZsPtANldXj9m+MAzJuuozUdd335W2FWAnqmxKC1zYQfT9f49Bw/nq6Fvs2E1DiNPEPGnX6Z4d2eLQUzmdIxkyWYqWkydLr+iIgiB4OZMGBb/+JunYFaaa2z8HVztrxk0t8FwJbprvX6NqdZI6mTSSLNH/HEXz/eB6NJxJWDMnH5gIzOXagPBEFAz5RYAMDZGt/qPaQVBhcVJHtc6yNlZg6FaUdTmaUAON0SzCTGqOXfYSmoJiJyh8FMGLAt/nX3IWcb7PhaBByoYybb3UgNTo6apOJfaVCfp8dMDfo2bLMsZvzL1QM7e5k+y0k0fyCfqfVtvYC75ZLOSB1NJ841osUQfpmMCofMjCAI8qZ21s0QkacYzIQIURTR3Or8w8jTVQaA+cPAumzS+2CmzWjCuUbLMZMfN2YD5mAsWm0+/nFWVyK1ZY+1zFcp9/DD7IxlN5EuWo3cpI7bmQMpS2dePeBLZsZgNKH4pLleZrSH9TKAuUg7OVYDUQQOh1l2xmgS5aNEqQAYsKmb8aGbjYgiU1CDmYULF2LUqFGIj49Heno6Zs6ciYMHD8r3V1dX45577kH//v0RHR2NHj164N5770VtbW0QrzowVuw4jYF/XYM1e8+2u8/TgXmSzsyaqW4yrzIQBCA5xv/D5uSOJift2dIx07g+lmDGw2MmadFidqLzPUZdJUvne2ZmT2ktmlqNSIxRo1+6511YgiCgX4ZlR1OY1c2ca9TDaBKhEOw759K40oCIvBTUYGbz5s0oKirCli1bsHbtWhgMBkyZMgWNjebjhjNnzuDMmTN45plnsHfvXixduhRr1qzBbbfdFszLDoj1B8oBAB/sbN/aK2/M9jCYkWoOfGnPrqo3Z2WSYzRQeZAJ8pY8a8YhM3O+sVXOCEmboisb9B51ZEmZGemYJ1ikYOqsF7U+Eqkle1R+MhRerpCw1s2EVzBTXms9zrT9XePgPCLyliqYL75mzRq7Py9duhTp6ekoLi7GpZdeisGDB+ODDz6Q7+/duzeeeOIJ/OpXv0JbWxtUqqBevl+dqGoCAGw7Xg2TSbT7QOvKzEyg6mUkckeTQ2bmWJU5K5Oti0LP5BgoBPMxxLlGvbx80JUzoZaZqWmGKIpeDeyTh+V5MF/GUf/MBADhtz1b7mTS2f/35UoDIvJWSNXMSMdHycmu/0Kvra1FQkKCy0BGr9ejrq7O7ivUmUwiTlgmuNY2G7DfYWZIq4dLJiWdGZwXqE4midTR5Liq4GiF+efvnR4HlVIhB1MVde4/0M5YalSCHcxIr9/UavRqFUOb0YQdJ6T5Mp4X/0r6Z1qOmcIsmHGc/ithZoaIvBUywYzJZML8+fMxfvx4DB482Oljqqqq8Pjjj+OOO+5w+TwLFy6ETqeTv/Ly8gJ1yX5TVtdi10a95Zj99mVPVxlIOrPSwJqZCcxyRleZGan4V5qvIn3AedKeHSo1M1FqpdyJ5U3dzE9n69Cgb0N8lAoDsxK8fl1p0nFZXQtqm3wb2OfKscoGfLy71Oelnx2pcBiYJ+HgPCLyVsgEM0VFRdi7dy+WL1/u9P66ujpcddVVKCwsxKOPPuryeRYsWIDa2lr569SpUwG6Yv85XmW/V2eLZd6IxNtgRt2JYKayPsDHTC5qZo5USMGMeVaL9AFX7sGOnlCpmQGsR01nvQhmpLbyUfnJUHpZLwOY39Nsy+seqvBvdmb+u7tx3/Ldctu4P5U5tGVLpGOmKgYzROShkAhm5s2bh9WrV2Pjxo3IzW2/5bi+vh5XXnkl4uPjsXLlSqjVaifPYqbVapGQkGD3FeqkYEb6S1yqm5F4WwCs7VTNjNSWHaiaGambyeGYScrMpJszM9IQtXI3x0xGkyhnb4KdmQGs7dmlXrRnb/FiH5Mr0rwZfx411bUYsKfUfPR7uML/bd/lDgPzJLbHTIHICBFR9xPUYEYURcybNw8rV67Ehg0bUFBQ0O4xdXV1mDJlCjQaDVatWoWoqOD/69vfTliCmWmDMxGnVbWrm/G1ANjVnBlRFO2CJVsBLwB2kpnRtxlRUm0ugO5jOWaS/rXubtZIZb0ebSYRSoXgtlC4K0jZobM1nmVmTCYR209Yghkf6mUk/QIQzPxwqgZSLHH6fJPfnlfiuMpAIgUzrUaTz3uuiCiyBDWYKSoqwttvv41ly5YhPj4eZWVlKCsrQ3Oz+YNACmQaGxvxxhtvoK6uTn6M0Ri6005La5rx7ZEqjx8vFf/2SY/DhflJAOzrZuSN2R62SndUAGw0ifjZv77FlS98hVPV7T+gKgO0MVvirGbm5LkmmEQgXquSP8ikY6YyN8GMVC+TmRDl0xGNv2V52Z59oKwetc0GxGqUGJztexZRXjjpx/ZsaYgfAJw+79tU4444bsyWaFVK6Cy/J6ybISJPBDWYWbx4MWprazFx4kRkZWXJX++++y4AYOfOndi6dSv27NmDPn362D0mlGthiv63Eze9vhV7Sz0b7icdM+WnxsrdLLZ1M3qDpZtJ7Vk3k1Qz42w304lzjdhTWotD5Q34+Svf4YhDjUWglkxKrJkZ6zGTNCyvV3qc3M7s6TGTtV4m+EdMgH17tiekluyR+cmdmusjHTMdKq/329HMzpIa+X/7O5hpMRhx3lKs7JiZAaxHruxoIiJPBHVQi7u/dCdOnBh2Z+ZtRhP2nTEHMbtKzmNwjq7DxxtNonzEkp8SK+8vsp0343VmpoMC4EM2xxDldXrc8O8t+M+tF2Fwjg5Gk4jqRksdQ8BrZqyZGcfiX8DzYybrjJngHzEB1rodT7uZfjxt/l25sGdSp163d1ocFIJ523Rlvb5dHYq3TCYRu0qsmZlSPx8zSUGKVqWQfydspcVrcbiigcEMEXkkJAqAu5OT1U0wGM0B2H4P6hdKzzfDYBShUSqQnRiNwdkJiNUo7epm/FkzI21XnjwwA0NydKhubMWNr23BjhPVqG5shUlaZRAboGMmJzUzjm3ZgPXo4Vxjq1wA7UyoDMyTSNdRVtvisi7J1jFLVq5vepybR3YsSq1Efqo5GDzgh7qZI5UNqG9ps9tg7c9FlmU2A/OcDRe0Ds7jfiYico/BjJ/ZLvs7cNb9wL7jlnqZHikxUCoEqJQKjLJ0tUh1M952M3U0AVhq3b2oIAn/+81ojMpPQn1LG379xjZ8vNu8SiEpQKsMANuaGZtjpkprzZAkKUYtf5B29K/z0hAZmCfJiNdCIQAGo4iqxo6zCqIo4pglkCuwyUr5yp9rDXZa6mVG9kxCnNacOfHnUZNcL+OiaJuD84jIGwxm/EzKMgDmzhJ3/zqXOpkKUq0fZo51M9YJwP47ZuqXEY+EKDX+c+toXNI3Fc0GI/726X4AgSv+BawTgBv0bWgzmiCKotPMjCBYu5M6qpsJtZoZlVIhX7e77dnnGltR39IGQTAfMXaWP9uzi22Cmdwk83vrz44mqZ0+Q9dxMMMCYCLyBIMZPztiM4+jsdXo9l+zxzsIZqS6Ga+DGRfdTK1tJvn1+ln+FR+tUeL1ORfiykGZ8uMCVfwLWDMzgDmgKatrQVOrESqFgJ4pMXaPlXb2dFQ3I9WmhEpmBgCypPZsN3UzxywZqZzEaER5WNzdEX92NO0scRbM+C8zIwUpGS5qs6SAkJkZIvIEgxk/O+zQHeS4Z8mR1JZt+y9zx7oZn9cZOAQzJ841os0kIl6rkrtuAHMr7L9+ORyzhucAsP4LPxDUSgViNOYP7rrmNnknU4+UGLkLS+KuPbtR34YaS0dMqBQAA0C2h4PzjluWa9oGsp3Rz6ajyZN6HVdqmlrlo7/heUly1qvUww4tT0iZGcclkxJmZojIGwxm/MhkEuUP5xE9EgEAB852/K/kE3JbtjUrYVs3s/VYtd+2Zku1FH0y4toVXaqUCjx7wzCsmjcef7xygEev4yu5CLjFILeG2x4xSdwdM0lHTPFRKrkLLBRkezg4T8rMOPvZfdEzOQYalQItBhNOdeJIaJelJbtXWiySYjXITTL/bgaiZsZV1xVbs4nIGwxm/Ki0phnNBiM0SgWmWI5tDnSQmTEYTThl+YBw/Ne5bd2M3sut2a52M0n1MtJxhCNBEDA0N9EvRx4dkduzmw1yBsDZB7q7Y6bSEKuXkUgrDdwNzjvm5IixM1RKhdwV1ZmOJqleZkQPc7t4IGpmXE3/lUiZmdpmg1+7qIioe2Iw40dHLIWs+akxGJxtni/T0YfKqeomGE0iotXKdl0dUjCz9Xi1/Jd55zMz5uvr6yKY6Sq2mRmp+LePk9Zkd8dMZ0Ksk0kiZWbczZqROpl6+aGTSSIFhSfPNbp5pGtSvYw1mPFvZkYURfm/qePGbIkuWi0fl0orNoiIXGEw40fSJNu+6fEYkGUOGE6ca0RTa5vTx0v1Mj1TYqBwGMVvWzfzo2WSsKdD87Qu5sxIbdn9MvxzrOEruT27uc2mk6n9B3qGfMzkKpgJ7cxMR1OA24wmeViivzIzAJBjyaKU+hh4tBlN2H2qBoC5+Nf2OSvr9X7JktS1tKHFMtXacZWBRBAE1s0QkccYzPiRNGOmd3ocUuO0SI3TQhStGRFHx6tcf5iplApcmG+um5Gnpap972ZqMRhx8pz59foFPTNjPmYqrWmW62F6OauZkacAd1wzE3qZGfP1VNTrXS77PG0ZlqhVKeSCYX/obOfRwfJ6NLUaEa9VyUdWSTFquWjb0zUNHZGCU120usMjzVTWzRCRhxjM+JF0zCR9CAy0ZGdcDc87YbOTyZkxDluUPc3MOKuZOVbZCKNJhC5aHbBVBZ6SMjO7LBmAtHitvFjQllQzU69vQ6O+fXarNMRWGUhSYjXQKBUQRddZJduWfMesXGd0tvNIGpZ3QY9E+boEQfBre7a7ehkJi4CJyFMMZvxEFEUclrqFLMHMAEurrKu6GemYqcDFwLQxvZLt/uzpokmNk0WTh22OmJyNj+9KUs3MbktthrMjJgCI06oQa8kIOAsKpJqUUDtmUigEORBzVQR8NAD1MoB9ZsaXvWbSckmpXsb6vP6rm5HastNd1MtIeMxERJ5iMOMnlQ161LW0QSFYj40GZCYAAPa7yMxIrbmuMjODc3TyhzngxaJJJzUzUlt2sIt/Adtlk+ZsS0etyRkutmcbTaL8oRhqx0yA++3Z/u5kkuQkmoOOBn2b3WZyT9lO/rUlBUmlNZ3vaJKCE2ZmiMhfGMz4iTT5t0dyjFwHIBUBHyirb/ev5BaDUc4suPpAU9vUzQCd62Y6WGa+vn6dXGjoDwkOM2GcdTJJpGDGceFgVYMeBqMIpUII+rGZM/L2bBeD845bAtleqf797xGtUSLFsiT0tJeBR1WDHiXVTRAE8zGTLSn75c/MjKviX4l1PxOXTXbGG98cxzvbSoJ9GUQBxWDGT6RgxvaDuU96HJQKAbXNhnbtxaeqmyCK5qOUjnYh2dbNeL2byejsmCkUMjP2wUzHmRlLe7bDcY1UE5KZEBWwpZidke1mpcGxKv8tmHTka32LVC/TLz2+XcDpz2Mmecmki+m/Eq406Lzzja14fPVP+MtHe53uaiPqLkLvUyBMWYMZa7CgVSnlehDHScDHbSb/dlTDYls34+vW7OZWo9wG3C+Aqwo85fhB2duDzIzjMdOZEC3+lVjbs9tnFRr1bdYuLj8fMwG+t2cXS/Nleia2u8+fg/OsG7NZMxNo5xpbAZiPZWubDUG+GqLAYTDjJ1JbtuORiVw34zAJ2NlOJmcG5+jkTh9djGcj+601M+ajraOVDRBFIDlWE9Alkp6SamYAIFqtRFYHxw1Se3a5w1FDqLZlSzrKzEiBbHKsBokx/t9Q7msWZdfJGgDAcIfiX/NzWtvN9W2dmzUjBXKu9jJJpOPDqgZ9p3ZNRbLa5lab/81ghrovBjN+csTFJFu5bqZdZsazgWlqpQJLbxmFV389Uk67u6NxaM0+aOmm6hsC9TKAfWamV1rHrcmZ8qwZx2AmdIt/gY4H50nFv4HIygC27dmeZ1Fa20z44XQNgPbFv4A58IpSm9vNz7pZoNkRo0lEpWWir7uamRTL8avBKKKGH8Q+Od9ofd8YzFB3xmDGD2qbDPK5vmMwM9CSmXHc0eTNxuThPZLkXU+eUDu0Zh8KoXoZwL5mxt2SRVcrDUpDPjNjvq7zTQY0t9pnMqQ1Bv7uZJL4Uqy7/6x5O3tijNppkGWeNdP5uplzDXoYTSIUAtxmCbUqJRIt2UjWzfjGNgisYzBD3RiDGT84UmkOFrJ0UYjTquzukzIzRysb7dLzJyyZGVdt2Z1hrZkxv550BBYK9TKAecu1pKNOJsC+Zsa2I8y6yiA0a2YSoqwzchyPmqRjJmdTj/0hN9n7wXm2yyVd1XD5o25GCkrT4rVQejAsMF2um2FHky9qmnjMRJGBwYwfOOtkkmQmREEXrYbRJMqPa241yn+puxqY1xlah5oZ6ZgpFNqyAXPmSBqP7y4zIw1Wa20z2f1lHOo1M4IgICvR+fZsab5QoDMzNU0GNDiZnOyMdblkosvHWGfN+J6Zketl3BwxSdI4a6ZTbP8/w2CGujMGM37gqvgXMH+oyZOALXUzUvGvLlqNpFj/F4Da7mZq1LfJHz6hcswEAHlJMRAEYHBOQoeP06qUSLIcNUgBYFNrG843mf9iDtVgBrAOzrP98BdFUc7MuJp83FnxUWq5aNzTjiapLXuEk3oZiT+OmaT/hukeBjNsz+6cmiaD0/9N1N0wmPEDV8W/koFZ9nUz7nYydZZUM2M0ifIqhdQ4bUACJ1+9dvOFePeOsejpQWbKsT1bKv6N16ratXmHEmmBpG3BbGW9Hg1686ToHikxAXttb4qAz9Y240xtCxQCMCw30e1zduaYqcLDvUwStmd3Tg0zMxQhGMz4gXR81DfdeebDcUfTcXknU2A+zGwnBe87UwsA6J8ZGkdMkh4pMbioINn9A2EbzJg/CEP9iEmSLR8zWTMZUidTblIMtCrPdm35wpvBeTstLdkDsxIQ61Dz5etzumKd/uvZiACuNOgc1sxQpGAw00lNrW3yX+6uMjMDsqQdTZZgRq6ZCEyAYbvDaW+pOZhxFWiFA+mDr6JdMBOaxb+SLMv1nbGpmQl0vYzEm8F5ey0B77C8xA4fJx0zlde1+DxNtrzes7ZsSRoLgDuFNTMUKRjMdJL04ZQSq0Gyi2Mc86Zq8/Cvynq9dWBeamAyM2qltUtk35k6yzWEczBj/uArC7fMjJNZM1JLvr+3ZTuSj4Q8KNaVCsSl41BXUuM00KoUMInt10t4qtzDvUwSFgB3jm2dDFuzqTtjMNNJ0hFTRyP5YzQqedLvwbJ6jwfm+UoQBDk7I23L7pcRWsdM3kh3qJkpDfGBeRIpM3O2plluK5eC30C1ZUu8KdY9YNnqPsBN674gCHLGx9e6GSnD4mkwk86amU6xPWaqsZkGTNTdMJjpJGmBo7t5KdIHxfYT1aiyTEANVAEw0H6lQd8wzsw4TgG2zpgJ7WBGysw0thpR12JukT4e4Om/klwPj5lqmwzyMVh/D+YQdaajqc1okrvQUjpYrmorLc78376+pQ0ths6tUYg0RpMo/94BPGai7o3BTCdZi3/dBTPmFP4X+8oAmI+lAtmJY1sELM26CVeOU4DP1IbHMVO0xtpWfra2GQajSV74GehjJimYqWrQdxgESB12OYnRHv0+dmZwnhTICAKQ5OFOqoRolfy7zKMm7zgGLwxmqDtjMNNJhzsYmGdL3tFkqU8IdAGobRFw3zA+YgKsRxKV9XoYjCa51TnUC4AB646mszUtKKluQptJRLRaiQwP92z5ShetlicQdzTk7oBcL+NZ5k4OZnwYnFdt2eCcFKPxaPovYD7aSovjUZMvpCMm6b1uMZg6vSSUKFQxmOmE1jYTTp4z/wvVXbeQtKNJEsgjJgBQq6wfFuFc/AuYZ+QoBMAkmmuOWo0mKATP6y6CSQq4SmuabbrYOl6u6Q/29S0dBTNSvUzHxb+SzhwznbMcr7oqlHdFmgLNzIx3pBkzmQlRkDZUMDtD3RWDmU44ea4RRpOIOK3K7dyM3KRo+V/KQNdmZvqHeTCjVAhyV8uuUzUAzIGMWhn6v762s2aOSctFA3zEJJECj47qZqRxAQM8zMzIw/h8CWYsmZkUL4MZa2aG7dneqLUc6yXFquUjRHY0UXcV1E+DhQsXYtSoUYiPj0d6ejpmzpyJgwcP2j2mpaUFRUVFSElJQVxcHK677jqUl5cH6YrtHbbpZHK1nE+iUAh2BZb5AdjJZEtjM5At3I+ZAGsWZpdl7H6o18tIbI+Z5DUGAQ5kJe6mAJtMotyW7WlmJi/JGpwZjN7NmpEyM54W/0qktRCOO66oY1L3UmK0Rq6Z40oD6q6CGsxs3rwZRUVF2LJlC9auXQuDwYApU6agsbFRfsz999+PTz75BCtWrMDmzZtx5swZzJo1K4hXbeVp8a9kgM0cj0DNmJHYFgCHcyeTRNrRI2VmwiWYyZYH5zXjqHTM1GWZmY6PmUqqm9BsMEKrUiDfw2nUqXFaaHycNSPVzHh7zCQv7OzEgstIJAUuuhjrri4eM1F35Xp2eRdYs2aN3Z+XLl2K9PR0FBcX49JLL0VtbS3eeOMNLFu2DJdffjkAYMmSJRg4cCC2bNmCMWPGBOOyZZ4W/0oGdmVmxjI4LycxGnEdjKgPF5k681GDlN0Ih+JfwJqZOVPTgmZLV1GvAE1+duRuCrBU/NsvIx4qD4/sFAoBuYnROFbViNPnm5GX7HlQXiUfM3m2ykAiBa5napiZ8YYUzCRGq1EXw2CGureQKjqorTWPVU9ONu/sKS4uhsFgwOTJk+XHDBgwAD169MD333/v9Dn0ej3q6ursvgLF28xMYbYOgDnA6GgHjj9ImZlwHpZny7H7J9RnzEhsN2dLBaxdXTPjKjMjFf96Ml/Glq+D86obLMGMl8dMOTZF1OQ5KXBJitEggZkZ6uZCJpgxmUyYP38+xo8fj8GDBwMAysrKoNFokJiYaPfYjIwMlJWVOX2ehQsXQqfTyV95eXkBuV6jScQxN9uyHY3okYi/XDUQ//j50IBcky2pADjcO5kkjp1L0kC6UJepM3eSGE3m4YWpcdou2/QtBXzl9c53KR2Qin+9DGZ8XThZ7WNmRspulde1yO8juSe1ZifymIkiQMgEM0VFRdi7dy+WL1/eqedZsGABamtr5a9Tp0756QrtnT7fBH2bCRqVQv4XsDuCIOD2S3phXJ/UgFyTrZ6WYyxPN1OHugydQ2YmKTyCGbVSIY/kBwI/+deWtEtJdFHfImVm3O1kciR3SXmZKalq9LE1O14LpUJAm0lke7YXpNZsXbSaBcDU7YVEMDNv3jysXr0aGzduRG5urnx7ZmYmWltbUVNTY/f48vJyZGZmOn0urVaLhIQEu69AkHcypcV5PACsK/1p+kCsmX8JLh+QHuxL8QvH1vdwKQAGrJkFIPCTf211tEupUd+Gk5ZpxL5nZrw8Zmr07ZhJpVTIKy141OQ5uWYmxtrNxNZs6q6CGsyIooh58+Zh5cqV2LBhAwoKCuzuHzlyJNRqNdavXy/fdvDgQZSUlGDs2LFdfbl2vC3+7WoalQIDMhPctoyHC9uamTitCglR4VPUbFusHOj5Qo5cbc8+VF4PUTRvpU6J8+7Yx5djJoPRJH+4ejtnBrC+h2drGcx4yvaYKZHHTNTNBfUToaioCMuWLcPHH3+M+Ph4uQ5Gp9MhOjoaOp0Ot912Gx544AEkJycjISEB99xzD8aOHRv0Tqa8pBhMGpCOi/KTgnodkSIxRg2NSoHWNhOyE6PCKkjLtsvMdG3w66oI+ECZb/UyAJCTaH7Os7UtaDOaPOqEOm/5YBUEc6bAW+bs1nl5ySi5Jx0zJUazZoa6v6AGM4sXLwYATJw40e72JUuWYO7cuQCAf/7zn1AoFLjuuuug1+sxdepUvPzyy118pe1dNTQLVw3NCvZlRAxBEJCRoMWp6uawOmICrHNSgK7PzLjann3grG/1MoC5hkWtFGAwiiiv13vUWSbPmPFiL5Mttmd7x2QS5cCFc2YoEgQ1mBFF950JUVFRWLRoERYtWtQFV0ShLCM+KiyDmWxL8bJSIaCHF3NZ/MFVfcv+TmRmFAoBOYnROHGuCaermzwKZs41+DYwTyK1ZzMz45n6ljZIf73qotVya3YNgxnqpkKiAJjIE5mWoCBcZsxIpAnMAzLj7SYzdwXrSgNrECCK1jUG3s6YkXi7cPKcj9N/JfLwQdbMeERaZRCjUUKrUjIzQ91e+FRRUsS77eICKBUCrh2eE+xL8Uqf9Di8e8cY5HZxVgawtrCX2dS3lNW1oLbZAKVC8LmA3dsiYGkvU6qXxcYSHjN5x3b6L2CuOQOA1jYTWgxGRKmVLr+XKBwxmKGwMbxHEob3CM+C69G9UoLyuunxUe3qW6Rheb3TYqFV+fah5m6JpSNf9zI5vl51Yys/jD0gz5ixFFvHaVVQKgQYLbU0fP+ou+ExE1E3plQI8hGNVAS83zIsz9NN2c7kJnuZmfFxxowkIVqFGI35A5h1M+5JbdlJloyMIAjyOAMeNVF3xGCGqJtzLAKW1xhk+b7qQqqZOeXh4DzpmMmXGTOA+cOYR02ekwIW6XgJAOtmqFtjMEPUzclHQpYsirzGoBOZGSmwKKttgcmDfUnWYybfamZsX5NFwO5JNTO6aGvwyJUG1J0xmCHq5qQi4NKaZujbjDha2Qigc5mZjHgtFAJgMIqoanC/L+mcjxuzbUkt7jxmcs+6ysCameHmbOrOGMwQdXO2bdRHKhpgNInQRavlfUe+UCkV8ibzM06WWDqSa2Z8PGYCbDuaGMy4I68yiLYGM9LkZQYz1B0xmCHq5mxnzcj1MpnxnV4J4WlwYTCa5A9Qb/dAOXu9sx4ET5GuxmnNDAuAqftiMEPUzdmuNNh/Vupk8v2ISZLl4bHPeUtWRiHYZwq8JR0zcXO2e1JmxlnNDDdnU3fEYIaom8vURUEhAK1GE745UgUAGODDTiZHnmZKpCOmpBgNFD7sZXJ8vTM1zR6tQolkzjMzUgFwa1CuiSiQGMwQdXNqpUKeNdOZbdmOPC3I9UfxL2BdZ9FiMLEjx41aJwXAbM2m7ozBDFEEsN1nJQhAvww/HDPJrdLuMjPmbidfp/9KotRKeR0Cj5pcE0XRmpmxO2ZiATB1XwxmiCKA1J4NAD2TYxCr7fwmkxwPC4CtmRnfi38l2dye7VaDvg1Gy+wfZmYoUjCYIYoAuTbBTGfWGNiSCoCrGvRobTO5fFy1H9qyJdk6djS5Ix3BRakVdjuYrMFMW1CuiyiQGMwQRQDbY6bODMuzlRyrgValgCgC5XWug4tznVwyaYuzZtyrdXLEBAC6GCmYaWUBNXU7DGaIIoA0OA/wX2bGdl9SRzUs8l4mPx4zsWbGNWfTfwFrZsZgFNFsMHb5dREFEoMZogiQY3fM5J/MDGA9ajrbwb4kvx4zcXCeW+flGTP2wUysRgmVpTWedTPU3TCYIYoAuUnRyE+JQb+MOPRIjnH/DR7yZJO1P1YZtH89ZmZccTZjBjBn0lgETN1V51saiCjkqZUKfHn/BCgEdGpwnSNPZs1Yj5n8UQBsfr3yuhYYjCaolfz3mKNaeS9T+/dbF63GucZWeQ4NUXfBvwmIIoRGpYDKzx/+WW6OfVrbTKhrMXfPJMd2vmYmNU4LtVKAyU3RcSRzVTMDcHM2dV8MZojIZ+6OfaT6jc7uZZIoFII8zZh1M85Jx0w6J8GMvNKAwQx1MwxmiMhn7o6ZpIF5ybGd28tky9MFl5FKzsw4OWaSsjVcNkndDYMZIvKZdMxU19KGBn37YWzSKoMUPxwxSXI8aAePZLXNlpqZDjIzXX3M9NyXB1H0v51oM7oerkjUGQxmiMhncVoVEqLMfQRnnQQX1X4cmCeR27M76KCKZNbMTGgEMyfPNeLFDUfw6Z6z+OF0bZe9LkUWBjNE1CnZHSyclI+Z/NDJJMnifqYOeVIz05XBzPLtp+T/XVLd2GWvS5GFwQwRdUpHRcDSMVNqADIzPGZqTxRFue06Kab9ey51M9V0UWu2wWjCih2n5T+fPNfUJa9LkYfBDBF1ijwFuMNjJv/XzLCbqb1mgxGtlrqUUKiZWb+/AlWWOUMAUMJghgKEwQwRdYo1U9I+uKiyHDP5Y2CeRAqeapsNTouOI9l5S8ZFo1Qg2mZjtkSqo+mqbqZ3tpUAAHqlxgIATlYzmKHAYDBDRJ0iLX90tp/Jn3uZJPFRasR3UHTsTlWDHp/tOYsfT9egqbV7BUM10l6mGDUEoX0rvHVzduCDmdPnm/DV4UoAwANT+gEwFwMHGjumIhPXGRBRp3Q0xC4Q3UyA+ajpQFk9ztS2oG+Gd4szH/rgR6zbXwEAEATz3qp+6fHomxGPfhlxuLhPKtITovx6vV2ltoNOJsD+mEkURacBj7+8t+M0RBEY1zsFl/RNA2DO1DXo2xCnDcxHz9eHK3HbWzswKDsBv7uiP8b3SQnoz0ihg8EMEXVKjk0BsOMHZJW8l8l/NTOA+WjrQFm91x1Noihix8nzAMwf7LXNBpyqbsap6masP2AOcHISo/H1Hy7z6w6rruJqyaRECmbaTCKaWo2IDVBQ0WY04T1LF9MvLuoBXbQaSTFqnG8yoORcEwqzE/z+mi0GI/68ci9a20zYVVKDX72xFWN6JeP3U/rjwvxkv78ehZagHjN99dVXuOaaa5CdnQ1BEPDRRx/Z3d/Q0IB58+YhNzcX0dHRKCwsxCuvvBKciyUipzISoiAIgL7NJGdiAPNepnrLXiZ/HjMBvk8BLq/To6bJAKVCwNY/TcLOh6/A8jvG4PEZg/DrMT2hVSlQWtOMQxX1fr3eriJ1KemcTP8FgGi1EmqlOUgL5EqDzYcqUVbXgqQYNaYOygAA9Egx180Eqj379a+PoaS6CenxWswdlw+NUoEtx6rx81e+x9wl27CHM266taAGM42NjRg2bBgWLVrk9P4HHngAa9aswdtvv439+/dj/vz5mDdvHlatWtXFV0pErmhUCqRZMi9nbIqApcBGqRDkjIC/WNvBveto2l9WB8BckBqlViI5VoMxvVLw67H5eHzmYIzulQIA2HL0nF+vt6vUdDD9FwAEQZADnUBuzn5nmzkrc92IXGhV5kLknskxAIATAehoOlPTjEUbjwIA/jR9IB792SBsenAibrwoD0qFgE0HK3HNv77Bb/9XjLoWrnLojoIazEybNg1/+9vfcO211zq9/7vvvsOcOXMwceJE5Ofn44477sCwYcOwbdu2Lr5SIupIljw4z5opkWbMJMX4by+TJMfNgktXDpw1Z1wGZDk/5hjTy3wc8f2x8Axm3NXMAIAu2ny0FKgi4LLaFmw4UA4A+MVFefLt+SnmYCYQs2ae+Gw/mg1GjMpPwowLsgGYA96Fs4Zi/QMTMGt4DgQB+GxPGR5c8QNEUfT7NVBwhXQ307hx47Bq1SqUlpZCFEVs3LgRhw4dwpQpU4J9aURkI9vJrBlp+q+/j5gAm2MmJx1UHdl/1pyZGZDpvGh4rCUzs/V4NUym8PvAk1cZuMjMAIGfNbNixymYRGBUfhL6pFvf50AdM313tAqf/ngWCgF47GeD2xX85qfG4rnZF2DFnWOhVgr4Yl853vz2hF+vgYIvpIOZl156CYWFhcjNzYVGo8GVV16JRYsW4dJLL3X5PXq9HnV1dXZfRBRYzlYayG3Zfpwx4/h6Z2tbvAo6DliOmQpdZGYG5+gQq1GipsmAg+XhVzcjHTPpnEz/legCOGvGZBLl9QU3XtTD7r6eAcjMtBlNeGzVTwCAm0b37LCw+ML8ZPzlqkIAwMLP9qPYUghO3UPIBzNbtmzBqlWrUFxcjGeffRZFRUVYt26dy+9ZuHAhdDqd/JWXl+fysUTkH84Kcs8FqC0bADJ15qLj1jaT/Dru6NuMOFppzgoMyHKemVErFXLny5YwPGqqkVcZuM/MSIGPP319pAqlNc1IiFJh+pAsu/ukmpkzNc1obfPPLJj/bjmJg+X1SIpR43eWWTYduXlsT1w1NAttJhHzlu20K1in8BaywUxzczP+9Kc/4bnnnsM111yDoUOHYt68eZg9ezaeeeYZl9+3YMEC1NbWyl+nTp1y+Vgi8g9nNSznLG3ZqX5uywbMQUd6vFR07NlR0+HyBhhNInTRamR2MEdmjOWo6fswLAKWjo4SXXQzAUCiJWsTiGOm5ZaJv9cOz0GUwwTitHgtotVKmETzQL3OqmrQ47m1hwAAv5/aX/65OiIIAp6aNQQFqbE4W9uCB97bHZbHidReyAYzBoMBBoMBCoX9JSqVSphMrqN6rVaLhIQEuy8iCqwsJ/uSAjUwT2I9avIsmDlQZj42GpgV3+EgtbG9w7du5nxTx91MgHXZpL+Dmcp6Pdb+ZC78vXF0j3b3C4JgPWryw1qDf6w5iPqWNgzOScAvRrV/PVfio9R4+aYR0KoU2HSwEos3H+30tVDwBTWYaWhowO7du7F7924AwPHjx7F7926UlJQgISEBEyZMwIMPPohNmzbh+PHjWLp0Kf7zn/+47H4iouCQCoDL61rkcfLSXqZABzPOdkI5c0Au/u34HziDsxMQq1GittkgB0DhwjpnxpMCYP+ucvh4dynaTCIuyEt0+R73sBw1dXbh5A+navBesTnr/tjPBkHpZbfcwKwEPD5jMADg2S8P4rujVZ26nkjwQfFp7DhRHezLcCmowcyOHTswfPhwDB8+HIB5rszw4cPx17/+FQCwfPlyjBo1CjfddBMKCwvx1FNP4YknnsBdd90VzMsmIgepcVqolQJMIlBebz5eqm6UjpkCFMx4OThPmjEz0EW9jESlVGBUQfi1aLcYjNC3ud6YLQlUN9PW4+YPuquHZrl8jD+KgEVRxKOf7IMoArOG52BkT9+m+94wKg8/H5kLkwjc+85uVNRzC7srW46dw+9W/IDb/7PDb/VO/hbUdQYTJ07ssN8/MzMTS5Ys6cIrIiJfKBQCMnVROFXdjLM1zchJjLY5ZvJ/zQzg3TGTKIrYL82YcZOZAcx1M5sOVmLLsXO47eKCzl1oF5GyMkqF0OHuIzmYafJv8eveUvOE3SE5OpePkdqzO7Nw8mhlA3aV1ECjVOChaQN8fh4AeHzGYOw5XYuD5fW4951d+N/tY7zO8kSCT388C8D8O/bt0Spc1j89yFfUXsjWzBBReMnWScc+5uBCnjMTqMyMF8dMlQ16VDe2QiEA/TxYTCnNm9kWQnUzp8834ZKnN2DxJuc1HvL032jnG7MlgcjMVDXocba2BYIADOogmMn3Q83MpoPmTdyjeyV3eiFotEaJRTeNQIxGiS3HqrFo45FOPV93ZDSJ+HxvmfznzyyBTahhMENEfmE7+0XfZkS9PjB7meTX03k+BVia/JufGotojdLNo4FB2QmI06pQ22yQj6eCbc3eMpyqbsa/vzoq1yXZkutlOjhiAqxHUP4MZvZYsjIFqbEdZoV6JkuD85p8DhKlYGain7IDfdLj5PqZ59cdCum6kGDYfqIaVQ16SAmrL38qh8HJ71+wMZghIr+wnTVju5cpIcq/e5kk2Ynm16us16PFYOzwsdLk34EeHDEBlrqZ/CQAodOi/dMZ889Q02TANicfuDUerDIAbIbmtbT5baz/3tPuj5gA838zlUJAa5sJ5T7UqDTq27DNUpszsX+a9xfqwnUjc3Ht8ByYROC+5bsDurfKFZNJhL6t49/jYPh8jzkTM/OCHKTGaVDbbMB3IfL/CVsMZojIL2yXP56z6WTy914mSXKsBmmWWTPSB5wrtm3ZnpLmzWw5Fhr/Ut93xpoh+nJfebv7a+Ulkx1nwqRgxmgS0aD3T0fTHg/qZQBzkJiTZP498aUI+Luj59BqNCEvORq9UmO9v9AOPD5zMPJTYlBa04w/fvBjl+5vEkUR1778LSY8vQmn/NC27i8mmyOma4ZlY+qgTAChedTEYIaI/ELKlJyttWZmAnXEBJjnlkweaD5q+PKnsg4fu9/Dtmxb0ryZbcfPwRjkupkWgxFHKhvkP3+5r6zdh62nmZkotRIalfmvfn8dNUnFv4PdBDOAtT3blyLgTQcrAACX9U/vsC7IF3FaFV68cTjUSgFr9pXhf1tL/Pr8HTlW1YgfTteirK4Fv/3fTreZxq5SXHIeFfV6xEepML5PKq6yTHX+4qeykDtqYjBDRH6RbTMFWNqYHajiX8mUQvO/FNf9VOGyBqO1zYSjlkDA1RoDZwqzEhCvVaGupU0OhoLlUHm9PL04RqPEmdoWu0wNANRI0389mIQrrzTww3HKuQa9vJNrUAe7kST5ckeTdxkIURRt6mX8d8Rka2huIv4w1dwh9fjqn3Cwi+YMbbXJ/u0prcVjn/zUJa/rjtTFdEVhBjQqBS4qSEZKrAY1TYaQW/fBYIaI/CLLUpB7vsmA0vPmotxAtWVLxvZOQaxGibK6Fvmow9HRygYYjCLio1Ty2gVP2M6bCfZf3FLgMiRHhwn9zB/kX+yzz0Z5sjFbkujHZZPS+94rNRbxHtRH+ToF+GhlA0prmqFRKTC2V6r3F+qh2y4uwIR+adC3mTBv2U40twY+S7L1uPn3a3yfFAgC8M62EqzYEdxVPCaTiDWWI6bpg80ZGZVSgamDLUdNe0LrqInBDBH5RUKUCrGWTqG9peYP30AeMwHmI5MJln+luzpqkjZlD8xM8PpoYqxcNxPsYMYcMAzKTsCUQRkA2tfN1HiwykDiz/Zsb46YAN+nAG88YGnJLkj2qCPNVwqFgGdvGIa0eC0OVzTg8U8DmyURRVH+/Sq6rA/un2xemPmXj/bK/92DYdep8yira0GcVoVL+lmDRymw+WJfudOuumBhMENEfiEIgnzUtNfyl3CggxnAetQk7QVyJLVle3PEJJGKgLcerw5q3YzUyVSYnYDL+2dApRBwsLweJ6qsdSeerDKQ+DOY8bT4V9LTcsx04lyjV0W2mw6Z62X81ZLdkdQ4Lf55wwUQBGDZ1hJ8ua/jmqzOOHmuCeV1emiUCozokYR5l/XBZf3NmaG7394ZlM4qAPhsj/lnnjwwHVqVNXgc0ysZybEaVDe2hkxxPMBghoj8SFo4eVo6ZgpwzQxgLgZVKQQcKm+w+3CX/ORD8a+kMNtcN1MfxLoZo8k6vXhQdgJ0MWo5yLLNRvlSM+OfzIz5ffE2M1Pf0uZxzU6jvg3bj58HAFwWoHoZRxf3TcWt483Tn/+75WTAXkc6YrogLxFRaiUUCgH/nH0BcpOiUVLdhN+t6PrN3iaTKLdkTx9iv55CpVRgqiU7+GkIHTUxmCEiv8lJtJ/ImhLgmhnAPCRudC9zbYuz7IwvbdkSpULARdKepiDN1jhe1YhmgxHRaiUKUuMAwOlRk7SewF03E2DdnF3TyWCmurFVnvg8KMezYDFao0RGgvn3wtO6Gaklu0dyDAr83JLdkRsvMm/j3nqs2m9t7I6k4l/pdxgwB6SLbxoJjUqBdfsrunyz9+7TNThT24JYjRKX9msfPEoBzpf7ykLmqInBDBH5jVQELAl0N5NEOmpyrJupatCjsl4PwcM1Bs5ILdrBqpuRM0tZ8fLeoCsKzcFMccl5VFoWe1ozMx4UAPtpCrDt5F9vhiNKk4A9bc/eeFA6Ykrze0t2R3qnxSI/JQatRhO+PlTp9+e3rZcZXZBid9+QXB3+72eDAJg3e397pOs2e0tZmUkDMxClbl+fNKZXCpJi1DjX2Op2xlNXYTBDRH4jTQGWJHdBzQwATLZ8uO84eR5VDXr5dqlepmdyDGI7GLPfkTE2e5qCUTcjFYEWZlkzH1m6aAzL1UEUgXX7y6FvM6LJ0nWTGN11x0zeFv9KenixPVsURWwOcEu2K4IgYNJA8+/Wuv0Vfn/+0+ebcaa2BSqFgBE9E9vdP3tUHq63bPZe8OGeLtlYLYqiXC8zfUim08eolQr5HxChctTEYIaI/Max9Tm1C46ZpNcdnJMAUQQ22HzoyJ1MWd7Xy0gGZiUgPkqFen1bULpLpOLfQdn2AcMUyzTWL/eVyUGJIADxUe6DNp2fWrP3yGsMvHt/eyZ7HswcqeialmxXJlkGM248WOH3YFbKygzN1SFG0/6/myAIePRng5Aap0FJdRPe7YJ27R9P16K0phkxGmWHxdbTh0pdTWVBHyoJMJghIj/KsglmVAoBCdG+ZUN84eyoSSqc9aX4V6JUCBjtQ92M0STi5U1H8PHuUp9H44uiaBPM2P8MUyzZqG+PnJMLrnXRao/WR/grM7PHx8xMz1Rp4aT7YyZpUN6YXikBbcl2ZVR+MhKiVKhubMWukvN+fe6tliMaKfvnTKxWhXsu7wsAeGn94YDPvZHmx1w2IN3pEZNkXO8UJMaoUdUQGkdNDGaIyG9sj5mSYzVdWt8g1ZF8fbgKTa3mYk0pM+NLW7at8X3MGQFpT40nPt1zFk+vOYj7lu/GvGW7UNfifeBQXqfHucZWKBUC+mfa/wx90uPQKzUWrUYTVu0+A8Cz4l/APxOAz9sU/3odzHiRmZHrZZwUonYFtVIhZyj8fdQk18t0EMwA5kLk3KRoVNTrsfS7E369BluiKOKzveZg5iqHLiZH5qMm8//nQmGAHoMZIvKbKLVSni3TVfUykgGZ8chLjoa+zYSvDlXBYDThcLl5jYGn27JduXpoNpQKAbtP1eBIRYP7bwDwfvFp+X9/uucsrnrxa+w+VePV60rHWr3TYtv9K1kQBFxh6WpauasUgGdt2ebHdT4zI2Vl8lNivN6MLk0BrqjXy4GnMw36Nmw/4f8t2d6SjprW73c+y8gXpTXNOH2+GUqFgJE9kzp8rEalkIfpLd50JGCzZ/aW1uFUdTOi1AqP3m+pq+nzvcE/amIwQ0R+lWVpz+6qTiaJIAi4YqB1gN7xqka0Gk2I06qQm+T5GgNn0uK1cmbgg52n3TzavGzz68Pm45EXbxyOvORonKpuxs8Xf4fXvjrm8dwQV/UyEulordaLTibA2ppd12LweYaJr0dMgDnoSrDU9pR00J793ZEqGIwieqZ0bUu2o4n90qFUCDhc0eD15GJXtlqyMoNzdIjzoDh95vAc9MuIQ11LG/79VWBataVi3ssHpDut4XE0rncqEqJUqGrQy0FnsDCYISK/yra0Z3fFjBlH0vyV9QfK5U6b/pnxHtWRuHPdyFwAwMqdpW7/FfrhzlKIInBRQTJ+Niwbq++5BFcNyUKbScQTn+3HbW9txzmbritXpJ1MhS4KmIfnJSIt3vo+e3vMJIpAvY/zU/Z6OfnXUX6q+4WTmyzt0BP7dW1LtiNdjBqj8s3Zk3V+ys5I82XGFCS7eaSZUiHg91P6AwCWfHsCFXUtfrkOSfHJ8/jv9ycAANMGd3zEJNGoFHIherCPmhjMEJFf5ViyIKlxXR/MXNgzCUkxatQ0GeSprQMyO1cvI5k0MB26aDXK6lrw3VHXMz9EUZSPmK63BEC6aDX+9cvheOLawdCqFNh4sBLTX/waRyo63sq876x1J5MzCoUg1woBnh8zaVVKRKnNf/372tHk7RoDR+52NImiiE0Hum6FgTuT5RZt/wQzWyyTfzsq/nV0RWEGhvdIRLPBiJc2HPHLdQDmwHTukm1obDVifJ8UXDnYeUu2M7OG5+CXo3vgmmHZfrseXzCYISK/+tWYnrjW8hdcV1MpFbh8gPlDZ1dJDYDOtWXb0qqU+JnlL2zbehhHxSfP43hVI2I0SrtR8IIg4KbRPfHxvPHonRaL8jo9nlt7yOXz1DYbcKraXGBb6CKYAaxdTYBne5kcH+tL3cz5xla5g2qQj8GMdXu2846mwxUNOFPbAo1K4dUHfqBIwcy249U+FXPbKqttwclzTVAIwIX5HdfL2BIEAX+YOgCAebO2P468DpbV49dvbEV9SxtG5SfhtZsvhFrpeWgwrk8qnrx2CEble5ZhChQGM0TkV73T4vDP2RegT3pcUF5fOmqS+LLGwJWfWzItX+wrc/mBJgU604dkOR3UNyAzAYtuGmF5nnKcsXQEOZJ2QeUkRneYcRnbO0WuufC0ZgboXEeTtEi0Z0qMVwGULesUYOcfyBssWZlgtWQ7yk+NRe+0WLSZrEP8fCXtYxqUrUO8l8XTY3un4JK+qWgzifjnOtfBsCeOVjbgpte34nyTAcNydXhz7iiPamVCEYMZIupWLu2bJh+hAED/TnYy2Rqaq0Of9Di0GEz47Mf2NQJNrW1YbbldCnycGZCZgDG9kmE0iVi2tcTpY/bZbMruiFallF9rWF6iJz8GAOukYGeZmeZWI8pqXddkdKb4V9LRFODik9V4Yd1hAMAVA4N/xCSRsjOd7WqStk2P9rBexpGUnflod6k8fsBbp6qbcNNrW1HVoMfArAS8detFXgdWoYTBDBF1K9EaJS7uY+486pEc41GniKcEQZADB2ddTV/sK0ODvg09kmNwkZu0+5yx+QDMxwUthvaD0FwNy3PmL1cNxPY/T8aIHp4fWSQ4OWZq1Lfh5U1HMO6p9Rj31Hqs+uGM0+/tbPEvAOSnmDMzpTXNMNgsKzTXb2xHs8GIS/qm4oZReT6/hr9JazM2HqzscMGiu+WLUmbG3XwZV4bk6nDVkCyIIvDMFwe9/v4zNc248bUtKKtrQZ/0OLx920Ue11uFKgYzRNTtzLjAXNtykY//8u3ItcNzoBCA7SfO40SVfb3Hih3mAOe6EbluO6iuKMxAli4K5xpbnXaCSDNmXLVl21IpFXZdTZ6wrZlp1Ldh8aajuPjvG/D0moM432SASQR+995ufOdkwWFni38BID1eC61KAaNJlI/ajlTU4+Y3t8n1G6/++kJoVcE/YpKM6GEuMK9tNqD4pPNpwF8frsSFT6zDL179HtWNre3ur6hrwbHKRggC3Aa8HXlgSj8oFQLW7a/A7977ATtLzrudNG0wmrDpYAVuen0rTp9vRn5KDJbdPhopQSjW9zcGM0TU7VwzLBvv3TkWD19d6PfnzkiIwsV9zZmfD22yM6fPN+G7o+cgCMB1I3PcPo9KqcCvxvQEALzlMNVV32aUh/O5O2bylRTMfLrnDC55eiP+vuYAzjcZkJ8Sg+duGIarhmTBYBRxx3+L7XZS1TS1yoXJgz0ItFxRKAS5o+nkuSbzscfrW1Hd2IohOTq8MXdUSNTK2FIqBFxm6axaf6D9NOA1e8tw29IdqGkyYMuxaly3+Lt2m8GlFQYDMxOg86LGyVHvtDjcfnEBAHOWcNbL32H6i9/gv1tOot6mnstoEvHtkSos+PBHXPTEOsxdsh3HqxqRkxiN//1mDNIToly9RFhhMENE3dJFBck+F6e6Yz1qKpWHzn1QbJ7CO653CnKTYjx6nl+MyoNGqcAPp2vtpgMfLm9Am0lEYowa2brAfNhI783e0jpUN7aiZ0oMnr1+GNY9MAGzRuTi2RuGYXRBMhr0bZi7ZDtOWYbb7S01H3/1SI7p1IcxYO1o2na8Gr98fQvK6/Tomx6Ht269yOupwl1F3qL9k33dzPvFp/Hb/xWj1WjCpAHpyEmMxvGqRsx6+Tu7nU7WI6bOZw0fmjYAH9w9DrNG5ECrUmD/2To8/NFejH5yPRZ8+KPlf6/DTa9vxTvbTuF8kwGpcRr8ekxPrLhrbLvFsOEsPMuWiYiCaEphBuKjVCitacaW4+cwpiAF7+80bzTuqPDXUUqcFlcPy8KHO0vx1ncncMHsCwDYHjElBGxYnNTl1SM5Bvdc3gfXDs+ByqYlN0qtxKs3X4jZ//4eB8rqMWfJNrx/1zib4t/OZ4x6Wupm/rXxiOXPMfjf7aO7fBWGNy7tlwq1UsCxqkYcq2xAr7Q4LPn2OB775CcAwA0X5uLJa4eguqkVty7djr2ldbjxtS144RfDMXVQpjwsb3RB59vNBcG8CmFkzyT89epCfLCzFMu2nsTRyka8s826YTsxRo1pgzNx9dBsjC5Itvvv3F0wmCEi8lKUWomrh2bjnW0l+KC4FApBwKnqZsRpVbhykGfTUyVzx+Xjw52lWP3jGfxp+kCkxWvl4l9Xk3/9YcqgTHzzx8uQkRDlcq6ILlqNpbdchFkvf4tjlY24del2efdWZzqZJFJmBjAvKX37ttEhf+wRH6XGmF4p+PpwFdbvr8AnP5yVW6Rvu7gAf7lqIARBQHp8FN69YyzmLduJjQcrcdfbxbhvUl8cthwf+rueKzFGg9suLsCt4/Ox7Xg1Pth5GgIETBuSifF9Ur2aHROOGMwQEfng5yNz8M62Eny+96y8LPHqoVle13kMzU3EBXmJ2H2qBsu3leCeSX3ltmxPin87w5PjsExdFP5z20W4bvH3dkdhnSn+lUidWqlxGrx9+2jkJXt2PBdskwak4+vDVXh+3SE0tpo70R64oh/uubyPXSYtVqvCazdfiIc/3od3tpXgeUu7+YDM+IBlnwRBwOheKT53SoWr7h2qEREFyIgeSShIjUVTqxGf7y0DAFx/oedHTLbmjssHALy99ST0bUZ5YJ4nbdldoU96PN6YcyG0KutHRmeKfyUjeyZj6S2jsPqeS9A7LThDFn0h1c1Igcwj1xTi3kl9nR4JqpQKPHntYDw4tb98m6/zZcg1BjNERD4QBAHXjbB2LfVKjfVqzoutaUMykRqnQXmdHq9/fRyNrUZoVYqgbop2dGF+Ml68cTiUCgGDcxKQ5KfMwsT+6cgMUJFzoOQlx+DCnklQKgQ8c/0w3DK+oMPHC4KAosv64KUbh2N8nxS5i438J6jBzFdffYVrrrkG2dnZEAQBH330UbvH7N+/Hz/72c+g0+kQGxuLUaNGoaTE+cRMIqKudO2IXEj/GL9uZK7PxbpalRK/vMi8y+rF9ZajiKyEkCvUnDooE5t+PxH/u21MsC8l6P5z20X49o+Xe1Xwfc2wbPzv9jHom+G/FRtkFtT/pzQ2NmLYsGFYtGiR0/uPHj2Kiy++GAMGDMCmTZvw448/4uGHH0ZUVHhF8UTUPeUkRuPGi3qgV1osbriwc5Nqfzm6J1QKAfo28/TYUDlicpTnh5bs7iBGowq7jFJ3FtQC4GnTpmHatGku7//zn/+M6dOn4+mnn5Zv6927d1dcGhGRR568dohfnidTF4WpgzPxqWW3UyA7mYi6m9DKYdowmUz49NNP0a9fP0ydOhXp6ekYPXq006MoIqLuQNrXBIRuZoYoFIVsMFNRUYGGhgY89dRTuPLKK/Hll1/i2muvxaxZs7B582aX36fX61FXV2f3RUQUDkblJ2HmBdm4uE+qX+a4EEWKkJ0zYzKZz41nzJiB+++/HwBwwQUX4LvvvsMrr7yCCRMmOP2+hQsX4rHHHuuy6yQi8hdBEPD8L4YH+zKIwk7IZmZSU1OhUqlQWGi/KG7gwIEddjMtWLAAtbW18tepU6dcPpaIiIjCX8hmZjQaDUaNGoWDBw/a3X7o0CH07Om6R1+r1UKrDf915kREROSZoAYzDQ0NOHLkiPzn48ePY/fu3UhOTkaPHj3w4IMPYvbs2bj00ktx2WWXYc2aNfjkk0+wadOm4F00ERERhRRBFEUxWC++adMmXHbZZe1unzNnDpYuXQoAePPNN7Fw4UKcPn0a/fv3x2OPPYYZM2Z4/Bp1dXXQ6XSora1FQgK7A4iIiMKBN5/fQQ1mugKDGSIiovDjzed3yBYAExEREXmCwQwRERGFNQYzREREFNYYzBAREVFYYzBDREREYY3BDBEREYU1BjNEREQU1hjMEBERUVhjMENERERhLWQXTfqLNOC4rq4uyFdCREREnpI+tz1ZVNDtg5n6+noAQF5eXpCvhIiIiLxVX18PnU7X4WO6/W4mk8mEM2fOID4+HoIg+PW56+rqkJeXh1OnTkX03ie+D2Z8H6z4XpjxfTDj+2DG98HKk/dCFEXU19cjOzsbCkXHVTHdPjOjUCiQm5sb0NdISEiI+F9MgO+DhO+DFd8LM74PZnwfzPg+WLl7L9xlZCQsACYiIqKwxmCGiIiIwhqDmU7QarV45JFHoNVqg30pQcX3wYzvgxXfCzO+D2Z8H8z4Plj5+73o9gXARERE1L0xM0NERERhjcEMERERhTUGM0RERBTWGMwQERFRWGMw46NFixYhPz8fUVFRGD16NLZt2xbsSwq4r776Ctdccw2ys7MhCAI++ugju/tFUcRf//pXZGVlITo6GpMnT8bhw4eDc7EBsnDhQowaNQrx8fFIT0/HzJkzcfDgQbvHtLS0oKioCCkpKYiLi8N1112H8vLyIF1x4CxevBhDhw6Vh16NHTsWn3/+uXx/pLwPjp566ikIgoD58+fLt0XCe/Hoo49CEAS7rwEDBsj3R8J7YKu0tBS/+tWvkJKSgujoaAwZMgQ7duyQ74+Evy/z8/Pb/U4IgoCioiIA/v2dYDDjg3fffRcPPPAAHnnkEezcuRPDhg3D1KlTUVFREexLC6jGxkYMGzYMixYtcnr/008/jRdffBGvvPIKtm7ditjYWEydOhUtLS1dfKWBs3nzZhQVFWHLli1Yu3YtDAYDpkyZgsbGRvkx999/Pz755BOsWLECmzdvxpkzZzBr1qwgXnVg5Obm4qmnnkJxcTF27NiByy+/HDNmzMC+ffsARM77YGv79u3497//jaFDh9rdHinvxaBBg3D27Fn565tvvpHvi5T3AADOnz+P8ePHQ61W4/PPP8dPP/2EZ599FklJSfJjIuHvy+3bt9v9PqxduxYAcP311wPw8++ESF676KKLxKKiIvnPRqNRzM7OFhcuXBjEq+paAMSVK1fKfzaZTGJmZqb4j3/8Q76tpqZG1Gq14jvvvBOEK+waFRUVIgBx8+bNoiiaf2a1Wi2uWLFCfsz+/ftFAOL3338frMvsMklJSeLrr78eke9DfX292LdvX3Ht2rXihAkTxPvuu08Uxcj5nXjkkUfEYcOGOb0vUt4DyR//+Efx4osvdnl/pP59ed9994m9e/cWTSaT338nmJnxUmtrK4qLizF58mT5NoVCgcmTJ+P7778P4pUF1/Hjx1FWVmb3vuh0OowePbpbvy+1tbUAgOTkZABAcXExDAaD3fswYMAA9OjRo1u/D0ajEcuXL0djYyPGjh0bke9DUVERrrrqKrufGYis34nDhw8jOzsbvXr1wk033YSSkhIAkfUeAMCqVatw4YUX4vrrr0d6ejqGDx+O1157Tb4/Ev++bG1txdtvv41bb70VgiD4/XeCwYyXqqqqYDQakZGRYXd7RkYGysrKgnRVwSf97JH0vphMJsyfPx/jx4/H4MGDAZjfB41Gg8TERLvHdtf3Yc+ePYiLi4NWq8Vdd92FlStXorCwMOLeh+XLl2Pnzp1YuHBhu/si5b0YPXo0li5dijVr1mDx4sU4fvw4LrnkEtTX10fMeyA5duwYFi9ejL59++KLL77A3XffjXvvvRdvvfUWgMj8+/Kjjz5CTU0N5s6dC8D//7/o9luziQKlqKgIe/futasLiDT9+/fH7t27UVtbi/fffx9z5szB5s2bg31ZXerUqVO47777sHbtWkRFRQX7coJm2rRp8v8eOnQoRo8ejZ49e+K9995DdHR0EK+s65lMJlx44YV48sknAQDDhw/H3r178corr2DOnDlBvrrgeOONNzBt2jRkZ2cH5PmZmfFSamoqlEplu4rr8vJyZGZmBumqgk/62SPlfZk3bx5Wr16NjRs3Ijc3V749MzMTra2tqKmpsXt8d30fNBoN+vTpg5EjR2LhwoUYNmwYXnjhhYh6H4qLi1FRUYERI0ZApVJBpVJh8+bNePHFF6FSqZCRkREx74WtxMRE9OvXD0eOHImo3wcAyMrKQmFhod1tAwcOlI/dIu3vy5MnT2LdunW4/fbb5dv8/TvBYMZLGo0GI0eOxPr16+XbTCYT1q9fj7FjxwbxyoKroKAAmZmZdu9LXV0dtm7d2q3eF1EUMW/ePKxcuRIbNmxAQUGB3f0jR46EWq22ex8OHjyIkpKSbvU+uGIymaDX6yPqfZg0aRL27NmD3bt3y18XXnghbrrpJvl/R8p7YauhoQFHjx5FVlZWRP0+AMD48ePbjWw4dOgQevbsCSBy/r6ULFmyBOnp6bjqqqvk2/z+O+HHQuWIsXz5clGr1YpLly4Vf/rpJ/GOO+4QExMTxbKysmBfWkDV19eLu3btEnft2iUCEJ977jlx165d4smTJ0VRFMWnnnpKTExMFD/++GPxxx9/FGfMmCEWFBSIzc3NQb5y/7n77rtFnU4nbtq0STx79qz81dTUJD/mrrvuEnv06CFu2LBB3LFjhzh27Fhx7NixQbzqwHjooYfEzZs3i8ePHxd//PFH8aGHHhIFQRC//PJLURQj531wxrabSRQj47343e9+J27atEk8fvy4+O2334qTJ08WU1NTxYqKClEUI+M9kGzbtk1UqVTiE088IR4+fFj83//+J8bExIhvv/22/JhI+PtSFM3dvj169BD/+Mc/trvPn78TDGZ89NJLL4k9evQQNRqNeNFFF4lbtmwJ9iUF3MaNG0UA7b7mzJkjiqK53fDhhx8WMzIyRK1WK06aNEk8ePBgcC/az5z9/ADEJUuWyI9pbm4Wf/vb34pJSUliTEyMeO2114pnz54N3kUHyK233ir27NlT1Gg0Ylpamjhp0iQ5kBHFyHkfnHEMZiLhvZg9e7aYlZUlajQaMScnR5w9e7Z45MgR+f5IeA9sffLJJ+LgwYNFrVYrDhgwQHz11Vft7o+Evy9FURS/+OILEYDTn82fvxOCKIqij5kjIiIioqBjzQwRERGFNQYzREREFNYYzBAREVFYYzBDREREYY3BDBEREYU1BjNEREQU1hjMEBERUVhjMENE3U5+fj6ef/75YF8GEXURBjNE1Clz587FzJkzAQATJ07E/Pnzu+y1ly5disTExHa3b9++HXfccUeXXQcRBZcq2BdAROSotbUVGo3G5+9PS0vz49UQUahjZoaI/GLu3LnYvHkzXnjhBQiCAEEQcOLECQDA3r17MW3aNMTFxSEjIwO//vWvUVVVJX/vxIkTMW/ePMyfPx+pqamYOnUqAOC5557DkCFDEBsbi7y8PPz2t79FQ0MDAGDTpk245ZZbUFtbK7/eo48+CqD9MVNJSQlmzJiBuLg4JCQk4IYbbkB5ebl8/6OPPooLLrgA//3vf5Gfnw+dTodf/OIXqK+vlx/z/vvvY8iQIYiOjkZKSgomT56MxsbGAL2bROQNBjNE5BcvvPACxo4di9/85jc4e/Yszp49i7y8PNTU1ODyyy/H8OHDsWPHDqxZswbl5eW44YYb7L7/rbfegkajwbfffotXXnkFAKBQKPDiiy9i3759eOutt7Bhwwb84Q9/AACMGzcOzz//PBISEuTX+/3vf9/uukwmE2bMmIHq6mps3rwZa9euxbFjxzB79my7xx09ehQfffQRVq9ejdWrV2Pz5s146qmnAABnz57FjTfeiFtvvRX79+/Hpk2bMGvWLHC1HVFo4DETEfmFTqeDRqNBTEwMMjMz5dv/9a9/Yfjw4XjyySfl2958803k5eXh0KFD6NevHwCgb9++ePrpp+2e07b+Jj8/H3/7299w11134eWXX4ZGo4FOp4MgCHav52j9+vXYs2cPjh8/jry8PADAf/7zHwwaNAjbt2/HqFGjAJiDnqVLlyI+Ph4A8Otf/xrr16/HE088gbNnz6KtrQ2zZs1Cz549AQBDhgzpxLtFRP7EzAwRBdQPP/yAjRs3Ii4uTv4aMGAAAHM2RDJy5Mh237tu3TpMmjQJOTk5iI+Px69//WucO3cOTU1NHr/+/v37kZeXJwcyAFBYWIjExETs379fvi0/P18OZAAgKysLFRUVAIBhw4Zh0qRJGDJkCK6//nq89tprOH/+vOdvAhEFFIMZIgqohoYGXHPNNdi9e7fd1+HDh3HppZfKj4uNjbX7vhMnTuDqq6/G0KFD8cEHH6C4uBiLFi0CYC4Q9je1Wm33Z0EQYDKZAABKpRJr167F559/jsLCQrz00kvo378/jh8/7vfrICLvMZghIr/RaDQwGo12t40YMQL79u1Dfn4++vTpY/flGMDYKi4uhslkwrPPPosxY8agX79+OHPmjNvXczRw4ECcOnUKp06dkm/76aefUFNTg8LCQo9/NkEQMH78eDz22GPYtWsXNBoNVq5c6fH3E1HgMJghIr/Jz8/H1q1bceLECVRVVcFkMqGoqAjV1dW48cYbsX37dhw9ehRffPEFbrnllg4DkT59+sBgMOCll17CsWPH8N///lcuDLZ9vYaGBqxfvx5VVVVOj58mT56MIUOG4KabbsLOnTuxbds23HzzzZgwYQIuvPBCj36urVu34sknn8SOHTtQUlKCDz/8EJWVlRg4cKB3bxARBQSDGSLym9///vdQKpUoLCxEWloaSkpKkJ2djW+//RZGoxFTpkzBkCFDMH/+fCQmJkKhcP1X0LBhw/Dcc8/h73//OwYPHoz//e9/WLhwod1jxo0bh7vuuguzZ89GWlpauwJiwJxR+fjjj5GUlIRLL70UkydPRq9evfDuu+96/HMlJCTgq6++wvTp09GvXz/85S9/wbPPPotp06Z5/uYQUcAIInsLiYiIKIwxM0NERERhjcEMERERhTUGM0RERBTWGMwQERFRWGMwQ0RERGGNwQwRERGFNQYzREREFNYYzBAREVFYYzBDREREYY3BDBEREYU1BjNEREQU1hjMEBERUVj7f4kR4IJXCQHQAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -342,11 +347,10 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "fig, axes = plt.subplots(nrows=1, ncols=1)\n", - "axes.plot(cost_values)\n", - "axes.set_xlabel(\"Iterations\")\n", - "axes.set_ylabel(\"Cost\")\n", - "axes.set_title(\"Cost convergence\")" + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" ] }, { @@ -364,12 +368,12 @@ "id": "0e49c243-8621-4893-9033-ddb45087f30d", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `get_results` method:" + "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `sample` method:" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "id": "778d3344-c84e-47cd-89b6-55d1df980813", "metadata": {}, "outputs": [ @@ -401,33 +405,33 @@ " \n", " \n", " \n", - " 128\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", - " 0.002441\n", - " 2.0\n", + " 289\n", + " {'x': [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]}\n", + " 0.000977\n", + " 1.0\n", " \n", " \n", - " 335\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", + " 399\n", + " {'x': [1, 1, 0, 1, 1, 0, 0, 0, 1, 0]}\n", " 0.000977\n", - " 2.0\n", + " 1.0\n", " \n", " \n", - " 204\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", - " 0.001953\n", + " 665\n", + " {'x': [1, 1, 0, 1, 1, 1, 0, 0, 0, 0]}\n", + " 0.000488\n", " 2.0\n", " \n", " \n", - " 191\n", - " {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", - " 0.001953\n", + " 325\n", + " {'x': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]}\n", + " 0.000977\n", " 2.0\n", " \n", " \n", - " 116\n", - " {'x_0': 1, 'x_1': 0, 'x_2': 1, 'x_3': 0, 'x_4'...\n", - " 0.002441\n", + " 436\n", + " {'x': [1, 1, 1, 0, 0, 0, 1, 0, 1, 0]}\n", + " 0.000977\n", " 2.0\n", " \n", " \n", @@ -435,43 +439,61 @@ "" ], "text/plain": [ - " solution probability cost\n", - "128 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.002441 2.0\n", - "335 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.000977 2.0\n", - "204 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.001953 2.0\n", - "191 {'x_0': 1, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.001953 2.0\n", - "116 {'x_0': 1, 'x_1': 0, 'x_2': 1, 'x_3': 0, 'x_4'... 0.002441 2.0" + " solution probability cost\n", + "289 {'x': [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]} 0.000977 1.0\n", + "399 {'x': [1, 1, 0, 1, 1, 0, 0, 0, 1, 0]} 0.000977 1.0\n", + "665 {'x': [1, 1, 0, 1, 1, 1, 0, 0, 0, 0]} 0.000488 2.0\n", + "325 {'x': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]} 0.000977 2.0\n", + "436 {'x': [1, 1, 1, 0, 0, 0, 1, 0, 1, 0]} 0.000977 2.0" ] }, - "execution_count": 24, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "optimization_result = combi.get_results()\n", + "optimization_result = combi.sample(optimized_params)\n", "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "ea88e99f-300d-4a18-b122-d0f2b31080a8", + "id": "b97b9f81-4d6d-4f65-947f-a8465643f442", "metadata": {}, "source": [ - "And the histogram:" + "We will also want to compare the optimized results to uniformly sampled results:" ] }, { "cell_type": "code", - "execution_count": 25, - "id": "26f85e77-a110-4e9c-a8ac-3354eae3c8da", + "execution_count": 14, + "id": "98d47d34-e05b-43c7-8f3d-bd407fe81342", + "metadata": {}, + "outputs": [], + "source": [ + "uniform_result = combi.sample_uniform()" + ] + }, + { + "cell_type": "markdown", + "id": "655de6d1-0a33-4b2b-ab35-053684a4a6d0", + "metadata": {}, + "source": [ + "And compare the histograms:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cb86cd46-9c67-4a2d-9acd-5bf90921e9db", "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -482,8 +504,22 @@ ], "source": [ "optimization_result[\"cost\"].plot(\n", - " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", + ")\n", + "plt.legend()\n", "plt.ylabel(\"Probability\", fontsize=16)\n", "plt.xlabel(\"cost\", fontsize=16)\n", "plt.tick_params(axis=\"both\", labelsize=14)" @@ -499,47 +535,37 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]\n", - "best_solution = [best_solution[f\"x_{v}\"] for v in range(len(best_solution))]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "2449caf6-d3c2-49b1-81cd-0e33e248cc18", - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 1, 1, 0, 0, 0, 1, 0, 1, 0]" + "[1, 1, 0, 0, 1, 0, 1, 0, 1, 0]" ] }, - "execution_count": 27, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "best_solution = optimization_result.solution[optimization_result.cost.idxmin()][\"x\"]\n", "best_solution" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "id": "fed415f4-67ed-4a85-9138-553c75972ac8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -585,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { "pycharm": { @@ -604,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "id": "1894641b-b166-47da-a3b8-5851d9042054", "metadata": {}, "outputs": [ @@ -614,7 +640,7 @@ "[1, 1, 0, 1, 1, 0, 0, 0, 1, 0]" ] }, - "execution_count": 30, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -625,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 20, "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da", "metadata": { "tags": [] diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod index 405b870cb..dcd187633 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod @@ -1,14 +1,5 @@ qstruct QAOAVars { - x_0: qbit; - x_1: qbit; - x_2: qbit; - x_3: qbit; - x_4: qbit; - x_5: qbit; - x_6: qbit; - x_7: qbit; - x_8: qbit; - x_9: qbit; + x: qbit[10]; } @@ -17,7 +8,7 @@ qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 3) { - phase (-(((((((((((((((((((((((1 - v.x_0) * (1 - v.x_1)) + ((1 - v.x_0) * (1 - v.x_5))) + ((1 - v.x_0) * (1 - v.x_6))) + ((1 - v.x_0) * (1 - v.x_7))) + ((1 - v.x_0) * (1 - v.x_8))) + ((1 - v.x_1) * (1 - v.x_2))) + ((1 - v.x_1) * (1 - v.x_4))) + ((1 - v.x_1) * (1 - v.x_5))) + ((1 - v.x_1) * (1 - v.x_6))) + ((1 - v.x_1) * (1 - v.x_9))) + ((1 - v.x_2) * (1 - v.x_3))) + ((1 - v.x_2) * (1 - v.x_4))) + ((1 - v.x_3) * (1 - v.x_6))) + ((1 - v.x_3) * (1 - v.x_8))) + ((1 - v.x_4) * (1 - v.x_6))) + ((1 - v.x_4) * (1 - v.x_7))) + ((1 - v.x_4) * (1 - v.x_8))) + ((1 - v.x_4) * (1 - v.x_9))) + ((1 - v.x_5) * (1 - v.x_6))) + ((1 - v.x_7) * (1 - v.x_8))) + ((1 - v.x_8) * (1 - v.x_9))) + (20 * (((((((((((v.x_0 + v.x_1) + v.x_2) + v.x_3) + v.x_4) + v.x_5) + v.x_6) + v.x_7) + v.x_8) + v.x_9) - 5) ** 2))), params[i]); + phase (-(((((((((((((((((((((((1 - v.x[0]) * (1 - v.x[1])) + ((1 - v.x[0]) * (1 - v.x[5]))) + ((1 - v.x[0]) * (1 - v.x[6]))) + ((1 - v.x[0]) * (1 - v.x[7]))) + ((1 - v.x[0]) * (1 - v.x[8]))) + ((1 - v.x[1]) * (1 - v.x[2]))) + ((1 - v.x[1]) * (1 - v.x[4]))) + ((1 - v.x[1]) * (1 - v.x[5]))) + ((1 - v.x[1]) * (1 - v.x[6]))) + ((1 - v.x[1]) * (1 - v.x[9]))) + ((1 - v.x[2]) * (1 - v.x[3]))) + ((1 - v.x[2]) * (1 - v.x[4]))) + ((1 - v.x[3]) * (1 - v.x[6]))) + ((1 - v.x[3]) * (1 - v.x[8]))) + ((1 - v.x[4]) * (1 - v.x[6]))) + ((1 - v.x[4]) * (1 - v.x[7]))) + ((1 - v.x[4]) * (1 - v.x[8]))) + ((1 - v.x[4]) * (1 - v.x[9]))) + ((1 - v.x[5]) * (1 - v.x[6]))) + ((1 - v.x[7]) * (1 - v.x[8]))) + ((1 - v.x[8]) * (1 - v.x[9]))) + (20 * (((((((((((v.x[0] + v.x[1]) + v.x[2]) + v.x[3]) + v.x[4]) + v.x[5]) + v.x[6]) + v.x[7]) + v.x[8]) + v.x[9]) - 5) ** 2))), params[i]); apply_to_all(lambda(q) { RX(params[3 + i], q); }, v); diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json index 2d6c6696b..2d889877c 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "u", - "s", - "r", - "rx", - "cy", - "sdg", + "id", "z", - "ry", - "rz", "sx", - "cz", - "h", - "cx", + "sdg", + "s", "x", - "sxdg", - "tdg", "t", - "p", "y", - "u2", "u1", - "id" + "cx", + "h", + "sxdg", + "cz", + "cy", + "p", + "r", + "rz", + "ry", + "tdg", + "rx", + "u2", + "u" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 2694895972 + "random_seed": 3857045423 } } diff --git a/applications/optimization/set_partition/set_partition.ipynb b/applications/optimization/set_partition/set_partition.ipynb index e5c00499d..2e8d0247c 100644 --- a/applications/optimization/set_partition/set_partition.ipynb +++ b/applications/optimization/set_partition/set_partition.ipynb @@ -51,7 +51,7 @@ "source": [ "## Building the Pyomo model from a graph input\n", "\n", - "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" + "We proceed by defining the Pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:" ] }, { @@ -64,8 +64,6 @@ "outputs": [], "source": [ "# we define a matrix which gets a set of integers s and returns a pyomo model for the partitioning problem\n", - "\n", - "\n", "def partite(s) -> pyo.ConcreteModel:\n", " model = pyo.ConcreteModel()\n", " SetSize = len(s) # the set size\n", @@ -94,7 +92,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "This is my list: [3, 10, 5, 5, 9, 6, 1, 4, 7, 1]\n" + "This is my list: [8, 8, 8, 5, 5, 6, 5, 6, 8, 11]\n" ] } ], @@ -140,7 +138,7 @@ "1 Objective Declarations\n", " cost : Size=1, Index=None, Active=True\n", " Key : Active : Sense : Expression\n", - " None : True : minimize : ((2*x[0] - 1)*3 + (2*x[1] - 1)*10 + (2*x[2] - 1)*5 + (2*x[3] - 1)*5 + (2*x[4] - 1)*9 + (2*x[5] - 1)*6 + 2*x[6] - 1 + (2*x[7] - 1)*4 + (2*x[8] - 1)*7 + 2*x[9] - 1)**2\n", + " None : True : minimize : ((2*x[0] - 1)*8 + (2*x[1] - 1)*8 + (2*x[2] - 1)*8 + (2*x[3] - 1)*5 + (2*x[4] - 1)*5 + (2*x[5] - 1)*6 + (2*x[6] - 1)*5 + (2*x[7] - 1)*6 + (2*x[8] - 1)*8 + (2*x[9] - 1)*11)**2\n", "\n", "3 Declarations: x_index x cost\n" ] @@ -159,7 +157,7 @@ "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` quantum object. Under the hood it tranlates the pyomo model to a quantum model of the qaoa algorithm, with cost hamiltonian translated from the pyomo model. We can choose the number of layers for the qaoa ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` python class. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm, with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { @@ -217,7 +215,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/6d0b5153-963f-45d5-84a0-ea52307aa923?version=0.61.0.dev7\n" + "Opening: https://nightly.platform.classiq.io/circuit/aec5c205-9edc-4e25-9812-ce23575c5542?version=0.62.0.dev9\n" ] } ], @@ -258,17 +256,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "5dcfa7ce-09e6-41ac-be81-298a281ba051", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Optimization Progress: 70%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 56/80 [02:08<00:55, 2.29s/it]\n" + ] + } + ], "source": [ - "cost_values = []\n", - "optimized_params = combi.optimize(\n", - " execution_preferences, maxiter=60, cost_trace=cost_values, quantile=0.7\n", - ")" + "optimized_params = combi.optimize(execution_preferences, maxiter=80, quantile=0.7)" ] }, { @@ -281,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "id": "6542ba4a-9493-4b01-8eea-8202d70b1f2c", "metadata": { "tags": [] @@ -293,13 +296,13 @@ "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 10, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -309,13 +312,10 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(nrows=1, ncols=1)\n", - "axes.plot(cost_values)\n", - "axes.set_xlabel(\"Iterations\")\n", - "axes.set_ylabel(\"Cost\")\n", - "axes.set_title(\"Cost convergence\")" + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" ] }, { @@ -333,14 +333,16 @@ "id": "96ea8543-29cb-4570-8741-7199dea8a948", "metadata": {}, "source": [ - "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `get_results` method:" + "We can also examine the statistics of the algorithm. In order to get samples with the optimized parameters, we call the `sample` method:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "bbc7c2ca-1d3a-4342-9a37-5b8fc8980fbe", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -370,77 +372,95 @@ " \n", " \n", " \n", - " 0\n", - " {'x_0': 0, 'x_1': 0, 'x_2': 1, 'x_3': 1, 'x_4'...\n", - " 0.019043\n", - " 1.0\n", + " 139\n", + " {'x': [0, 0, 1, 1, 1, 1, 1, 1, 0, 0]}\n", + " 0.001953\n", + " 1.183052e-271\n", " \n", " \n", - " 281\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'...\n", + " 278\n", + " {'x': [1, 1, 0, 0, 0, 0, 1, 1, 1, 0]}\n", " 0.000977\n", - " 1.0\n", + " 1.183052e-271\n", " \n", " \n", - " 59\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", - " 0.004395\n", - " 1.0\n", + " 545\n", + " {'x': [1, 0, 1, 0, 0, 0, 0, 0, 1, 1]}\n", + " 0.000488\n", + " 1.183052e-271\n", " \n", " \n", - " 282\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'...\n", - " 0.000977\n", - " 1.0\n", + " 515\n", + " {'x': [0, 0, 1, 0, 1, 0, 1, 1, 0, 1]}\n", + " 0.000488\n", + " 1.183052e-271\n", " \n", " \n", - " 284\n", - " {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'...\n", - " 0.000977\n", - " 1.0\n", + " 438\n", + " {'x': [0, 0, 0, 1, 1, 1, 0, 0, 1, 1]}\n", + " 0.000488\n", + " 1.183052e-271\n", " \n", " \n", "\n", "" ], "text/plain": [ - " solution probability cost\n", - "0 {'x_0': 0, 'x_1': 0, 'x_2': 1, 'x_3': 1, 'x_4'... 0.019043 1.0\n", - "281 {'x_0': 0, 'x_1': 1, 'x_2': 1, 'x_3': 0, 'x_4'... 0.000977 1.0\n", - "59 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.004395 1.0\n", - "282 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 0, 'x_4'... 0.000977 1.0\n", - "284 {'x_0': 0, 'x_1': 1, 'x_2': 0, 'x_3': 1, 'x_4'... 0.000977 1.0" + " solution probability cost\n", + "139 {'x': [0, 0, 1, 1, 1, 1, 1, 1, 0, 0]} 0.001953 1.183052e-271\n", + "278 {'x': [1, 1, 0, 0, 0, 0, 1, 1, 1, 0]} 0.000977 1.183052e-271\n", + "545 {'x': [1, 0, 1, 0, 0, 0, 0, 0, 1, 1]} 0.000488 1.183052e-271\n", + "515 {'x': [0, 0, 1, 0, 1, 0, 1, 1, 0, 1]} 0.000488 1.183052e-271\n", + "438 {'x': [0, 0, 0, 1, 1, 1, 0, 0, 1, 1]} 0.000488 1.183052e-271" ] }, - "execution_count": 11, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "optimization_result = combi.get_results()\n", + "optimization_result = combi.sample(optimized_params)\n", "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "2a6d978a-f2a2-46a0-8deb-bdfa6c9c3405", + "id": "a55201b4-31b1-4477-a9fd-06a35352f0a1", + "metadata": {}, + "source": [ + "We will also want to compare the optimized results to uniformly sampled results:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "acc7cbeb-b7a8-41a4-8922-0280d29b58af", + "metadata": {}, + "outputs": [], + "source": [ + "uniform_result = combi.sample_uniform()" + ] + }, + { + "cell_type": "markdown", + "id": "ccca8f9a-b252-4aeb-a92f-96deb4109d87", "metadata": {}, "source": [ - "And the histogram:" + "And compare the histograms:" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "85fdf055-cb4e-4756-8983-3a607cc22382", + "execution_count": 35, + "id": "8b0f949c-015a-4bbe-8bc1-8feb0e6c4480", "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -451,8 +471,22 @@ ], "source": [ "optimization_result[\"cost\"].plot(\n", - " kind=\"hist\", bins=30, edgecolor=\"black\", weights=optimization_result[\"probability\"]\n", + " kind=\"hist\",\n", + " bins=50,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=50,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", + ")\n", + "plt.legend()\n", "plt.ylabel(\"Probability\", fontsize=16)\n", "plt.xlabel(\"cost\", fontsize=16)\n", "plt.tick_params(axis=\"both\", labelsize=14)" @@ -468,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { "tags": [] @@ -480,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "f349d9fb-132e-4ca0-8433-db1e2d61efa1", "metadata": { "tags": [] @@ -490,15 +524,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "P1= [3, 10, 6, 7] , total sum: 26\n", - "P2= [5, 5, 9, 1, 4, 1] , total sum: 25\n", - "difference= 1\n" + "P1= [5, 6, 5, 8, 11] , total sum: 35\n", + "P2= [8, 8, 8, 5, 6] , total sum: 35\n", + "difference= 0\n" ] } ], "source": [ - "p1 = [mylist[i] for i in range(len(mylist)) if best_solution[f\"x_{i}\"] == 0]\n", - "p2 = [mylist[i] for i in range(len(mylist)) if best_solution[f\"x_{i}\"] == 1]\n", + "p1 = [mylist[i] for i in range(len(mylist)) if best_solution[\"x\"][i] == 0]\n", + "p2 = [mylist[i] for i in range(len(mylist)) if best_solution[\"x\"][i] == 1]\n", "print(\"P1=\", p1, \", total sum: \", sum(p1))\n", "print(\"P2=\", p2, \", total sum: \", sum(p2))\n", "print(\"difference= \", abs(sum(p1) - sum(p2)))" @@ -514,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { "pycharm": { @@ -532,21 +566,21 @@ " Variables:\n", " x : Size=10, Index=x_index\n", " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : 1.0 : 1 : False : False : Binary\n", - " 1 : 0 : 1.0 : 1 : False : False : Binary\n", + " 0 : 0 : 0.0 : 1 : False : False : Binary\n", + " 1 : 0 : 0.0 : 1 : False : False : Binary\n", " 2 : 0 : 0.0 : 1 : False : False : Binary\n", " 3 : 0 : 1.0 : 1 : False : False : Binary\n", - " 4 : 0 : 0.0 : 1 : False : False : Binary\n", - " 5 : 0 : 0.0 : 1 : False : False : Binary\n", - " 6 : 0 : 0.0 : 1 : False : False : Binary\n", - " 7 : 0 : 0.0 : 1 : False : False : Binary\n", + " 4 : 0 : 1.0 : 1 : False : False : Binary\n", + " 5 : 0 : 1.0 : 1 : False : False : Binary\n", + " 6 : 0 : 1.0 : 1 : False : False : Binary\n", + " 7 : 0 : 1.0 : 1 : False : False : Binary\n", " 8 : 0 : 1.0 : 1 : False : False : Binary\n", - " 9 : 0 : 1.0 : 1 : False : False : Binary\n", + " 9 : 0 : 0.0 : 1 : False : False : Binary\n", "\n", " Objectives:\n", " cost : Size=1, Index=None, Active=True\n", " Key : Active : Value\n", - " None : True : 1.0\n", + " None : True : 0.0\n", "\n", " Constraints:\n", " None\n" @@ -564,19 +598,7 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "2e5c1b07-6060-455d-81ed-e48a2207c81b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "classical_solution = [pyo.value(set_partition_model.x[i]) for i in range(len(mylist))]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da", "metadata": { "tags": [] @@ -586,15 +608,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "P1= [5, 9, 6, 1, 4] , total sum: 25\n", - "P2= [3, 10, 5, 7, 1] , total sum: 26\n", - "difference= 1\n" + "P1= [8, 8, 8, 11] , total sum: 35\n", + "P2= [5, 5, 6, 5, 6, 8] , total sum: 35\n", + "difference= 0\n" ] } ], "source": [ - "p1 = [mylist[i] for i in range(len(mylist)) if classical_solution[i] == 0]\n", - "p2 = [mylist[i] for i in range(len(mylist)) if classical_solution[i] == 1]\n", + "p1 = [mylist[i] for i in range(len(mylist)) if round(classical_solution[i]) == 0]\n", + "p2 = [mylist[i] for i in range(len(mylist)) if round(classical_solution[i]) == 1]\n", "print(\"P1=\", p1, \", total sum: \", sum(p1))\n", "print(\"P2=\", p2, \", total sum: \", sum(p2))\n", "print(\"difference= \", abs(sum(p1) - sum(p2)))" diff --git a/applications/optimization/set_partition/set_partition.qmod b/applications/optimization/set_partition/set_partition.qmod index 2b37221c3..703f5e6db 100644 --- a/applications/optimization/set_partition/set_partition.qmod +++ b/applications/optimization/set_partition/set_partition.qmod @@ -1,14 +1,5 @@ qstruct QAOAVars { - x_0: qbit; - x_1: qbit; - x_2: qbit; - x_3: qbit; - x_4: qbit; - x_5: qbit; - x_6: qbit; - x_7: qbit; - x_8: qbit; - x_9: qbit; + x: qbit[10]; } @@ -17,7 +8,7 @@ qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 3) { - phase (-((((((((((((6 * v.x_0) + (20 * v.x_1)) + (10 * v.x_2)) + (10 * v.x_3)) + (18 * v.x_4)) + (12 * v.x_5)) + (2 * v.x_6)) + (8 * v.x_7)) + (14 * v.x_8)) + (2 * v.x_9)) - 51) ** 2), params[i]); + phase (-((((((((((((16 * v.x[0]) + (16 * v.x[1])) + (16 * v.x[2])) + (10 * v.x[3])) + (10 * v.x[4])) + (12 * v.x[5])) + (10 * v.x[6])) + (12 * v.x[7])) + (16 * v.x[8])) + (22 * v.x[9])) - 70) ** 2), params[i]); apply_to_all(lambda(q) { RX(params[3 + i], q); }, v); diff --git a/applications/optimization/set_partition/set_partition.synthesis_options.json b/applications/optimization/set_partition/set_partition.synthesis_options.json index df25d261f..ac5cce353 100644 --- a/applications/optimization/set_partition/set_partition.synthesis_options.json +++ b/applications/optimization/set_partition/set_partition.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "cz", + "u", + "u1", + "cx", "x", "u2", - "ry", - "rz", + "sdg", "s", "r", - "z", - "u", - "t", - "u1", + "cz", "id", + "cy", "sxdg", - "sdg", - "h", - "sx", + "ry", "rx", - "tdg", + "rz", "y", + "sx", + "tdg", + "t", + "z", "p", - "cx", - "cy" + "h" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 2863520222 + "random_seed": 1468870560 } } diff --git a/applications/physical_systems/ising_model/ising_model.ipynb b/applications/physical_systems/ising_model/ising_model.ipynb index cdd184f07..1aa183609 100644 --- a/applications/physical_systems/ising_model/ising_model.ipynb +++ b/applications/physical_systems/ising_model/ising_model.ipynb @@ -44,7 +44,7 @@ "source": [ "## 1. Define the Optimization Problem\n", "\n", - "We created a python pyomo model to describe an Ising model as optimization problem over the configuration of spins. We take a simple manifestation of the 1d Ising model, described as [6]:\n", + "We created a python Pyomo model to describe an Ising model as optimization problem over the configuration of spins. We take a simple manifestation of the 1d Ising model, described as [6]:\n", "\n", "$H(\\sigma) = -\\sum\\limits _{i,j}J\\sigma_{i}\\sigma_{j}-\\sum\\limits _{i} h\\sigma_{i}$\n", "\n", @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "b2c6406f-8aa3-4674-870e-13b5a36b50dc", "metadata": {}, "outputs": [], @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "0b28f2dc-26ec-4975-9e1f-41adfdd75cb4", "metadata": { "pycharm": { @@ -160,7 +160,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/0b452fbf-fc23-4a09-b800-6ad828f47e96?version=0.61.0.dev7\n" + "Opening: https://nightly.platform.classiq.io/circuit/40d4cb3e-8ee4-4b6e-be8b-bd120647f993?version=0.62.0.dev9\n" ] } ], @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "4f779a6a-dae4-4235-92be-f90820fbfbeb", "metadata": { "tags": [] @@ -203,27 +203,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "6daf89c6-2e5c-41b5-b162-dfd048014919", "metadata": { "tags": [] }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "CPU times: user 8.15 s, sys: 211 ms, total: 8.36 s\n", - "Wall time: 2min 39s\n" + "Optimization Progress: 101it [03:14, 1.92s/it] \n" ] } ], "source": [ - "%%time\n", - "cost_values = []\n", - "optimized_params = combi.optimize(\n", - " execution_preferences, maxiter=100, cost_trace=cost_values, quantile=0.7\n", - ")" + "optimized_params = combi.optimize(execution_preferences, maxiter=100, quantile=0.7)" ] }, { @@ -236,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "id": "370e71c6-764e-4296-882a-d5daa14d3176", "metadata": { "tags": [] @@ -248,13 +243,13 @@ "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -266,11 +261,10 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "fig, axes = plt.subplots(nrows=1, ncols=1)\n", - "axes.plot(cost_values)\n", - "axes.set_xlabel(\"Iterations\")\n", - "axes.set_ylabel(\"Cost\")\n", - "axes.set_title(\"Cost convergence\")" + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" ] }, { @@ -280,16 +274,18 @@ "source": [ "## 4. Present Quantum Results\n", "\n", - "we call the `get_results` method to get samples with the optimzied parameters. We hereby present the optimization results. Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n", + "We call the `sample` method to get samples with the optimzied parameters. We hereby present the optimization results. Since this is a quantum solution with probabilistic results, there is a defined probability for each result to be obtained by a measurement (presented by an histogram), where the solution is chosen to be the most probable one.\n", "\n", "We remind that in the notation of the solution \"0\" indicate \"-1\" spin value, and \"1\" indicates \"1\" spin value." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "id": "2c35bcde-2d6e-4ed0-aa70-325dbf5a7846", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -320,32 +316,32 @@ " \n", " \n", " 0\n", - " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", - " 0.608887\n", + " {'z': [0, 0, 0, 0, 0, 0]}\n", + " 0.589355\n", " -180.0\n", " \n", " \n", - " 21\n", - " {'z_0': 1, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", - " 0.007812\n", + " 1\n", + " {'z': [0, 0, 0, 0, 0, 1]}\n", + " 0.045410\n", " -100.0\n", " \n", " \n", - " 19\n", - " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'...\n", - " 0.008789\n", + " 2\n", + " {'z': [0, 0, 1, 0, 0, 0]}\n", + " 0.041992\n", " -100.0\n", " \n", " \n", - " 16\n", - " {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 1, 'z_4'...\n", - " 0.010254\n", + " 3\n", + " {'z': [0, 0, 0, 1, 0, 0]}\n", + " 0.038574\n", " -100.0\n", " \n", " \n", - " 14\n", - " {'z_0': 0, 'z_1': 0, 'z_2': 1, 'z_3': 0, 'z_4'...\n", - " 0.011230\n", + " 4\n", + " {'z': [0, 1, 0, 0, 0, 0]}\n", + " 0.038086\n", " -100.0\n", " \n", " \n", @@ -353,84 +349,119 @@ "" ], "text/plain": [ - " solution probability cost\n", - "0 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.608887 -180.0\n", - "21 {'z_0': 1, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.007812 -100.0\n", - "19 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4'... 0.008789 -100.0\n", - "16 {'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 1, 'z_4'... 0.010254 -100.0\n", - "14 {'z_0': 0, 'z_1': 0, 'z_2': 1, 'z_3': 0, 'z_4'... 0.011230 -100.0" + " solution probability cost\n", + "0 {'z': [0, 0, 0, 0, 0, 0]} 0.589355 -180.0\n", + "1 {'z': [0, 0, 0, 0, 0, 1]} 0.045410 -100.0\n", + "2 {'z': [0, 0, 1, 0, 0, 0]} 0.041992 -100.0\n", + "3 {'z': [0, 0, 0, 1, 0, 0]} 0.038574 -100.0\n", + "4 {'z': [0, 1, 0, 0, 0, 0]} 0.038086 -100.0" ] }, - "execution_count": 18, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "optimization_result = combi.get_results()\n", + "optimization_result = combi.sample(combi.optimized_params)\n", "optimization_result.sort_values(by=\"cost\").head(5)" ] }, { "cell_type": "markdown", - "id": "06d70826-8ba1-4705-ae87-1a3c4ba5d69a", + "id": "242936e1-23e9-44c0-ae34-62dfbf61ce4b", "metadata": {}, "source": [ - "Best Solution:" + "We will also want to compare the optimized results to uniformly sampled results:" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "1b2775b1-7b12-4a0a-9931-eacdee4e5127", + "execution_count": 11, + "id": "a26c4460-f73e-436e-ae26-26eaa485930e", "metadata": {}, + "outputs": [], + "source": [ + "uniform_result = combi.sample_uniform()" + ] + }, + { + "cell_type": "markdown", + "id": "70a99eba-a999-40c4-8030-b84cde4c5d0a", + "metadata": {}, + "source": [ + "And compare the histograms:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "67b0d5fa-49ee-4245-9953-3b4a937a3c08", + "metadata": { + "tags": [] + }, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "{'z_0': 0, 'z_1': 0, 'z_2': 0, 'z_3': 0, 'z_4': 0, 'z_5': 0}" + "
" ] }, - "execution_count": 32, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "optimization_result.sort_values(by=\"cost\").iloc[0].solution" + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=30,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", + ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=30,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", + ")\n", + "plt.legend()\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" + ] + }, + { + "cell_type": "markdown", + "id": "06d70826-8ba1-4705-ae87-1a3c4ba5d69a", + "metadata": {}, + "source": [ + "Best Solution:" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "81d6f1dd-7e65-4118-bc8b-9ee662c72a68", - "metadata": { - "tags": [] - }, + "execution_count": 13, + "id": "1b2775b1-7b12-4a0a-9931-eacdee4e5127", + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[]], dtype=object)" + "{'z': [0, 0, 0, 0, 0, 0]}" ] }, - "execution_count": 19, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "optimization_result.sort_values(by=\"cost\").iloc[0].solution" ] }, { diff --git a/applications/physical_systems/ising_model/ising_model.qmod b/applications/physical_systems/ising_model/ising_model.qmod index a40985b4c..6108af2c7 100644 --- a/applications/physical_systems/ising_model/ising_model.qmod +++ b/applications/physical_systems/ising_model/ising_model.qmod @@ -1,10 +1,5 @@ qstruct QAOAVars { - z_0: qbit; - z_1: qbit; - z_2: qbit; - z_3: qbit; - z_4: qbit; - z_5: qbit; + z: qbit[6]; } @@ -13,7 +8,7 @@ qfunc main(params: real[10], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 5) { - phase (-(((((((((((((40.0 * v.z_0) + (40.0 * v.z_1)) + (40.0 * v.z_2)) + (40.0 * v.z_3)) + (40.0 * v.z_4)) + (40.0 * v.z_5)) + ((10 - (20 * v.z_0)) * ((2 * v.z_1) - 1))) + ((10 - (20 * v.z_0)) * ((2 * v.z_5) - 1))) + ((10 - (20 * v.z_1)) * ((2 * v.z_2) - 1))) + ((10 - (20 * v.z_2)) * ((2 * v.z_3) - 1))) + ((10 - (20 * v.z_3)) * ((2 * v.z_4) - 1))) + ((10 - (20 * v.z_4)) * ((2 * v.z_5) - 1))) - 120.0), params[i]); + phase (-(((((((((((((40.0 * v.z[0]) + (40.0 * v.z[1])) + (40.0 * v.z[2])) + (40.0 * v.z[3])) + (40.0 * v.z[4])) + (40.0 * v.z[5])) + ((10 - (20 * v.z[0])) * ((2 * v.z[1]) - 1))) + ((10 - (20 * v.z[0])) * ((2 * v.z[5]) - 1))) + ((10 - (20 * v.z[1])) * ((2 * v.z[2]) - 1))) + ((10 - (20 * v.z[2])) * ((2 * v.z[3]) - 1))) + ((10 - (20 * v.z[3])) * ((2 * v.z[4]) - 1))) + ((10 - (20 * v.z[4])) * ((2 * v.z[5]) - 1))) - 120.0), params[i]); apply_to_all(lambda(q) { RX(params[5 + i], q); }, v); diff --git a/applications/physical_systems/ising_model/ising_model.synthesis_options.json b/applications/physical_systems/ising_model/ising_model.synthesis_options.json index cb806fc57..9c23bd269 100644 --- a/applications/physical_systems/ising_model/ising_model.synthesis_options.json +++ b/applications/physical_systems/ising_model/ising_model.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "ry", - "tdg", - "id", - "t", + "u1", "cx", - "sx", - "h", - "u2", - "y", - "s", "p", - "sdg", - "sxdg", + "cz", + "h", "x", - "cy", - "u1", + "sx", + "y", + "tdg", "rz", + "cy", + "id", + "s", "r", - "cz", + "t", "z", "rx", - "u" + "u", + "sdg", + "ry", + "sxdg", + "u2" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 234206522 + "random_seed": 2916469319 } } From 9e6d990119492f885e1922f05d1436c7c7b29d69 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Mon, 16 Dec 2024 11:04:37 +0200 Subject: [PATCH 19/38] updated timeouts --- tests/resources/timeouts.yaml | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 1305243ce..93113b94e 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -125,12 +125,10 @@ HW_4_Bill_Wisotsky.ipynb: 300 hw_aware_synthesis.ipynb: 20 hybrid_qnn_for_subset_majority.ipynb: 120 inplace_prepare_int.qmod: 10 -integer_linear_programming.ipynb: 296 -integer_linear_programming.qmod: 340 -ising_model.ipynb: 48 -ising_model.qmod: 48 -knapsack_binary.ipynb: 72 -knapsack_binary.qmod: 52 +integer_linear_programming.ipynb: 400 +integer_linear_programming.qmod: 400 +ising_model.ipynb: 300 +ising_model.qmod: 300 knapsack_integer.ipynb: 116 knapsack_integer.qmod: 116 learning_optimization.ipynb: 80 @@ -140,16 +138,16 @@ linear_pauli_rotations.ipynb: 20 linear_pauli_rotations.qmod: 10 link_monitoring.ipynb: 76 link_monitoring.qmod: 88 -max_clique.ipynb: 276 -max_clique.qmod: 260 +max_clique.ipynb: 300 +max_clique.qmod: 300 max_cut.ipynb: 36 max_cut.qmod: 32 max_independent_set.ipynb: 60 max_independent_set.qmod: 64 max_induced_k_color_subgraph.ipynb: 1028 max_induced_k_color_subgraph.qmod: 1008 -max_k_vertex_cover.ipynb: 184 -max_k_vertex_cover.qmod: 148 +max_k_vertex_cover.ipynb: 300 +max_k_vertex_cover.qmod: 300 maximum_float_example.qmod: 10 maximum_integer_example.qmod: 10 mcx.ipynb: 236 @@ -197,8 +195,8 @@ patching_managment.ipynb: 36 PHASE.qmod: 10 phase_kickback.ipynb: 1000 phase_kickback.qmod: 1000 -portfolio_optimization.ipynb: 96 -portfolio_optimization.qmod: 112 +portfolio_optimization.ipynb: 400 +portfolio_optimization.qmod: 400 Preparation_for_Week2_Git_GitHub.ipynb: 20 prepare_bell_state.ipynb: 20 prepare_bell_state.qmod: 10 @@ -284,8 +282,8 @@ second_quantized_hamiltonian.qmod: 40 Session1.ipynb: 20 set_cover.ipynb: 1440 set_cover.qmod: 1216 -set_partition.ipynb: 152 -set_partition.qmod: 160 +set_partition.ipynb: 350 +set_partition.qmod: 350 shor.ipynb: 104 shor.qmod: 88 shor_modular_exponentiation.ipynb: 300 From cf5171383fe709dd07db8a332997e8308c02aee3 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Mon, 16 Dec 2024 11:45:23 +0200 Subject: [PATCH 20/38] with portfolio optimization as well --- .../portfolio_optimization.ipynb | 488 ++++++------- .../portfolio_optimization.qmod | 678 +----------------- ...tfolio_optimization.synthesis_options.json | 44 +- tests/resources/timeouts.yaml | 3 +- 4 files changed, 260 insertions(+), 953 deletions(-) diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb index 938fcaaba..5fff5f122 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb +++ b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb @@ -56,12 +56,6 @@ "execution_count": 1, "id": "952d49b3-5dc6-41a1-8822-0622df536cf7", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:07.327339Z", - "iopub.status.busy": "2024-05-07T15:06:07.326871Z", - "iopub.status.idle": "2024-05-07T15:06:07.542423Z", - "shell.execute_reply": "2024-05-07T15:06:07.541631Z" - }, "tags": [] }, "outputs": [], @@ -77,7 +71,7 @@ "source": [ "# The Portfolio Optimization Problem Parameters\n", "\n", - "First we define the parameters of the optimization problem, which include the expected return vector, the covariance matrix, the total budget and the asset-specific budgets:" + "First we define the parameters of the optimization problem, which include the expected return vector, the covariance matrix and the total budget:" ] }, { @@ -85,12 +79,6 @@ "execution_count": 2, "id": "6212e51c", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:07.547954Z", - "iopub.status.busy": "2024-05-07T15:06:07.546656Z", - "iopub.status.idle": "2024-05-07T15:06:07.552727Z", - "shell.execute_reply": "2024-05-07T15:06:07.552195Z" - }, "pycharm": { "name": "#%%\n" }, @@ -99,17 +87,15 @@ "outputs": [], "source": [ "returns = np.array([3, 4, -1])\n", - "# fmt: off\n", "covariances = np.array(\n", " [\n", - " [ 0.9, 0.5, -0.7],\n", - " [ 0.5, 0.9, -0.2],\n", - " [-0.7, -0.2, 0.9],\n", + " [0.9, 0.5, -0.7],\n", + " [0.5, 0.9, -0.2],\n", + " [-0.7, -0.2, 0.9],\n", " ]\n", ")\n", - "# fmt: on\n", - "total_budget = 6\n", - "specific_budgets = [2, 2, 2]" + "\n", + "total_budget = 6" ] }, { @@ -127,12 +113,6 @@ "execution_count": 3, "id": "42650f31-8efe-4ca9-8ed6-f5d9d440bee4", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:07.556775Z", - "iopub.status.busy": "2024-05-07T15:06:07.555783Z", - "iopub.status.idle": "2024-05-07T15:06:07.565348Z", - "shell.execute_reply": "2024-05-07T15:06:07.564551Z" - }, "tags": [] }, "outputs": [], @@ -141,14 +121,11 @@ "num_assets = len(returns)\n", "\n", "# setting the variables\n", - "portfolio_model.w = pyo.Var(\n", - " range(num_assets),\n", - " domain=pyo.Integers,\n", - " bounds=lambda _, idx: (0, specific_budgets[idx]),\n", - ")\n", + "portfolio_model.w = pyo.Var(range(num_assets), domain=pyo.Integers, bounds=(0, 6))\n", + "\n", "w_array = list(portfolio_model.w.values())\n", "\n", - "# setting the constraint\n", + "# global budget constraint\n", "portfolio_model.budget_rule = pyo.Constraint(expr=(sum(w_array) <= total_budget))\n", "\n", "# setting the expected return and risk\n", @@ -163,102 +140,80 @@ }, { "cell_type": "markdown", - "id": "c671eeac-5b61-4ab4-9e92-cfcb2ed8170b", + "id": "ea100320-dab7-4a4c-aed9-e08e3a70fb78", "metadata": { "tags": [] }, "source": [ "## Setting Up the Classiq Problem Instance\n", "\n", - "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):" + "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` python class. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm, with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian." ] }, { "cell_type": "code", "execution_count": 4, - "id": "c044e30f-2b4f-41ef-9bc0-11b951cb88db", + "id": "9503e674-3194-44ad-b248-fffa6fc3a9b2", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:07.570439Z", - "iopub.status.busy": "2024-05-07T15:06:07.569306Z", - "iopub.status.idle": "2024-05-07T15:06:10.280055Z", - "shell.execute_reply": "2024-05-07T15:06:10.275821Z" - }, "tags": [] }, "outputs": [], "source": [ "from classiq import *\n", - "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n", + "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n", "\n", - "qaoa_config = QAOAConfig(num_layers=1)" - ] - }, - { - "cell_type": "markdown", - "id": "dce2689a-d47f-42c0-9468-5faa4da21d20", - "metadata": {}, - "source": [ - "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" + "combi = CombinatorialProblem(pyo_model=portfolio_model, num_layers=3, penalty_factor=10)\n", + "\n", + "qmod = combi.get_model()" ] }, { "cell_type": "code", "execution_count": 5, - "id": "7c4a6a91-aece-4a3b-9fa2-e7f76c90f296", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:10.287616Z", - "iopub.status.busy": "2024-05-07T15:06:10.287145Z", - "iopub.status.idle": "2024-05-07T15:06:10.292183Z", - "shell.execute_reply": "2024-05-07T15:06:10.291510Z" - }, - "pycharm": { - "name": "#%%\n" - }, - "tags": [] - }, + "id": "86d783b7-820a-40a8-90af-aedb735b7678", + "metadata": {}, "outputs": [], "source": [ - "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)" + "write_qmod(qmod, \"portfolio_optimization\")" ] }, { "cell_type": "markdown", - "id": "a78aeea0-246b-4a58-9d7f-94cded812348", + "id": "696f5c22-eb43-488a-a948-aa057c005bed", "metadata": {}, "source": [ - "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:" + "## Synthesizing the QAOA Circuit and Solving the Problem\n", + "\n", + "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" ] }, { "cell_type": "code", "execution_count": 6, - "id": "d3988443-adff-4196-981f-d22307de17c9", + "id": "25bc3abe-18a1-4e41-ab3d-084cd49d463b", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:10.297071Z", - "iopub.status.busy": "2024-05-07T15:06:10.295820Z", - "iopub.status.idle": "2024-05-07T15:06:12.171845Z", - "shell.execute_reply": "2024-05-07T15:06:12.171188Z" - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/79e25a43-a916-476c-a108-022dfec85da8?version=0.62.0.dev9\n" + ] + } + ], "source": [ - "qmod = construct_combinatorial_optimization_model(\n", - " pyo_model=portfolio_model,\n", - " qaoa_config=qaoa_config,\n", - " optimizer_config=optimizer_config,\n", - ")" + "qprog = combi.get_qprog()\n", + "show(qprog)" ] }, { "cell_type": "markdown", - "id": "0d64c135-ec3b-490e-ae60-2d72f0ff633c", + "id": "b06ce4de-2fce-4360-bade-6af4d2c0558d", "metadata": {}, "source": [ "We also set the quantum backend we want to execute on:" @@ -267,111 +222,48 @@ { "cell_type": "code", "execution_count": 7, - "id": "e0b0013d-5152-4221-a8d5-d35820c3a878", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:12.174681Z", - "iopub.status.busy": "2024-05-07T15:06:12.174351Z", - "iopub.status.idle": "2024-05-07T15:06:12.190978Z", - "shell.execute_reply": "2024-05-07T15:06:12.190335Z" - }, - "tags": [] - }, + "id": "fcddad02-a283-4812-a22e-289da66dcae7", + "metadata": {}, "outputs": [], "source": [ - "from classiq.execution import ClassiqBackendPreferences\n", + "from classiq.execution import *\n", "\n", - "qmod = set_execution_preferences(\n", - " qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n", + "execution_preferences = ExecutionPreferences(\n", + " backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n", ")" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "804e8e99-2e63-43df-9168-f1cb8497bdad", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:12.193308Z", - "iopub.status.busy": "2024-05-07T15:06:12.192932Z", - "iopub.status.idle": "2024-05-07T15:06:12.333982Z", - "shell.execute_reply": "2024-05-07T15:06:12.333340Z" - } - }, - "outputs": [], - "source": [ - "write_qmod(qmod, \"portfolio_optimization\")" - ] - }, { "cell_type": "markdown", - "id": "b098aa8a-e47f-474a-b5a4-75f3b98d2628", + "id": "a22a913a-720f-4ca9-806f-3ae70a5ba57a", "metadata": {}, "source": [ - "## Synthesizing the QAOA Circuit and Solving the Problem\n", - "\n", - "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:" + "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "73cccd8c", + "execution_count": 8, + "id": "ff59d10e-c215-42a2-b232-df77fd775aff", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:12.336365Z", - "iopub.status.busy": "2024-05-07T15:06:12.336172Z", - "iopub.status.idle": "2024-05-07T15:06:14.856707Z", - "shell.execute_reply": "2024-05-07T15:06:14.855692Z" - }, - "pycharm": { - "name": "#%%\n" - }, "tags": [] }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Opening: https://platform.classiq.io/circuit/5c17bd7f-2b65-4deb-94a5-6fb8708345d1?version=0.41.0.dev39%2B79c8fd0855\n" + "Optimization Progress: 61it [04:44, 4.67s/it] \n" ] } ], "source": [ - "qprog = synthesize(qmod)\n", - "show(qprog)" - ] - }, - { - "cell_type": "markdown", - "id": "45f19792-d1ec-48b4-bc15-0fe881e48cd9", - "metadata": {}, - "source": [ - "We now solve the problem by calling the `execute` function on the quantum program we have generated:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c6607c43-7b33-44dd-9e38-b90c2db888e5", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:14.861747Z", - "iopub.status.busy": "2024-05-07T15:06:14.861264Z", - "iopub.status.idle": "2024-05-07T15:06:18.624172Z", - "shell.execute_reply": "2024-05-07T15:06:18.623365Z" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "result = execute(qprog).result_value()" + "optimized_params = combi.optimize(execution_preferences, maxiter=60, quantile=0.7)" ] }, { "cell_type": "markdown", - "id": "90c622a1-d8ae-47ac-a924-86d5b73bbd45", + "id": "2d0f1a15-90ac-44dc-ae1b-b3c1c62d3d92", "metadata": {}, "source": [ "We can check the convergence of the run:" @@ -379,55 +271,75 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "7cff5dd0-312b-45b3-8af3-0fd6fa04b6e9", + "execution_count": 9, + "id": "858ea131-6109-47cc-ba00-55ba0e8f09f9", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:18.628597Z", - "iopub.status.busy": "2024-05-07T15:06:18.628037Z", - "iopub.status.idle": "2024-05-07T15:06:18.667398Z", - "shell.execute_reply": "2024-05-07T15:06:18.666685Z" - }, + "scrolled": true, "tags": [] }, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ - "" + "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7hMemTJkCjuO8fhYsWOD1uuPHj2POnDnQ6XQoLCzEfffdB7vdHufVEwRBEASRqCRENdaWLVvw8ssvo7KyssdzN910Ex599FHhd51OJ/zb4XBgzpw5KC4uxnfffYe6ujrMmzcParUaTz75ZFzWThAEQRBEYiN7ZKejowNXX301Xn31VeTk5PR4XqfTobi4WPgRl4Z/9tln2LNnD95++22MHz8es2fPxmOPPYalS5fCarXG82MQBEEQBJGgyC52Fi5ciDlz5mD69Ol+n//Xv/6F/Px8jBkzBosWLUJnZ6fw3MaNGzF27FgUFRUJj82cORNGoxG7d+8OuE+LxQKj0ej1QxAEQRBEciJrGmvFihXYunUrtmzZ4vf5X/3qVxg4cCBKSkqwY8cOPPDAA9i/fz8++OADAEB9fb2X0AEg/F5fXx9wv0uWLMHixYuj9CkIgiAIgkhkZBM7NTU1uPPOO7Fu3TqkpaX53ebmm28W/j127Fj069cP06ZNw6FDhzBkyJCI971o0SLcc889wu+s3TRBEARBEMmHbGms6upqNDY24vTTT4dKpYJKpcKGDRvw4osvQqVSweFw9HjNxIkTAQAHDx4EABQXF6OhocFrG/Z7cXFxwH1rtVphNASNiCAIgiCI5EY2sTNt2jTs3LkT27ZtE37OOOMMXH311di2bRuUSmWP12zbtg0A0K9fPwBAVVUVdu7cicbGRmGbdevWwWAwoKKiIi6fgyAIgiCIxEa2NFZmZibGjBnj9VhGRgby8vIwZswYHDp0CMuXL8eFF16IvLw87NixA3fffTfOPfdcoUR9xowZqKiowLXXXounn34a9fX1ePDBB7Fw4UJotVo5PhZBEIRfLHYHVAoFlIrgAwsJgog+sldjBUKj0eDzzz/HjBkzMHLkSPz2t7/F5ZdfjtWrVwvbKJVKrFmzBkqlElVVVbjmmmswb948r748BEEQctNtc+C8p9fjF69slHspBJGScDzP83IvQm6MRiOysrLQ3t6eFP6dE62duOed7bjxnEGYOTqwd4kgiPhwsLED05/bAKWCw8EnZoPjKLpDENFA6vU7ITooE9Hl8z0N+P5oC3RaJYkdgkgAOiyuETYOJw+bg4dGRWKHIOJJwqaxiMhpMbu6R7eaqYs0QSQCZotnXl+XtWelKUEQsYXEThLS0mn1+i9BEPLSIRY7NhI7BBFvSOwkIZ7Ijk3mlRAEAQAd3R6x02m1B9mSIIhYQGInCWFip8Nih9XulHk1BEGYrRTZIQg5IbGThIgjOm2UyiII2TGJIjvdJHYIIu6Q2ElCTomMyeTbIQj5ERuUO8mgTBBxh8ROksHzPFpFAod8OwQhP1SNRRDyQmInyTB22+FwevpEtlJkhyBkx0TVWAQhKyR2kowWn946vr8TBBF/KLJDEPJCYifJ8BU3ZFAmCPmhPjsEIS8kdpIM367JLeTZIQjZ6bB4BA6JHYKIPyR2kgzf6ivy7BCBaOu0Ym+dUe5lpASUxiIIeSGxk2SwNJbCPWeQxA4RiIXLt2L2C//DwcYOuZeS9Ig7KJPYIYj4Q2InyWBprAG5Oq/fCcKXnxpcIudos1nmlSQ/Xn12KI1FEHGHxE6SwSI7Qwv1rt8pskP4ged5QQgbu8nXFUt4nkeHaFxEN0V2CCLukNhJMpjYGVzgEjttZFAm/GDstsPu7sckHmVARJ9OqwO8p/UVGZQJQgZI7CQZLJIzpCADgKuZGQ0DJXwRtygwdpEgjiXiFBZA4yIIQg5I7CQZLDVRnpchmJSp1w7hS4vZIvyb0lixxeQjdiiyQxDxh8ROksGGgObptcjWaQCQb4foyakOz3eC0lixxTeyQ1PPCSL+kNhJImwOp3Dhys3QIEenBkDDQImeeKWxKLITUzoojUUQskNiJ4lgPXUUHJCVrkZuhsbrcYJgiKN9xi6K7MQS1mNH6c4rU58dgog/JHaSCBbBydZpoFRwnjQW9dohfGjpoMhOvDC7y87z3DcflMYiiPhDYieJOOU2nbL0Va5b7JBBmfBFLIDJsxNbWGSnIFMLgNJYBCEHJHaSCBbZYemrnAwW2aE7d8KbU1R6HjfYEFAmdrpsDvDixjsEQcQcEjtJBPNhCGKHGZQpskP44GtQpotv7GDVWPl6rfBYt416XxFEPCGxk0QwH4ZvZIfEDuGLWOzYHDxdfGMIq8ZikR2Aeu0QRLwhsZNEMFGT4/bqMM8ODQMlfPE1rZvIpBwzmNgxpKmhUblOuSR2CCK+kNhJItgFzBPZcaWxqKkgIabL6hAuthql6xRAFVmxgxmU9Vol0tVKAECXlUzhBBFPSOwkEa09PDvuaiwyKAeF53ks+Gc1rnx5I+yO5E/nsKo9jVKBoixXaqWdeu3EDFZ6rk9TicRO8n/PCCKRUMm9ACJ6sBEAzKvDRA8bBspC6IQ3e+qM+HR3PQDgRGsXyvMzZF5RbBFHAA1pagBdFNmJISyNlaFRQadxix1KYxFEXKGrXxLBIjuseZkhTU3DQCXw8c464d+pcNFnYidHEDvUayeWeNJYKqS5IzudlMYiiLhCYidJ4HnecxFzp68Uoi7KrZ3JfxGPBJ7n8d8dIrGTAukc9j3Jy9DAkO4K7lKvndjBSs/1aSqkuyM71EWZIOILiZ0kodPqgMXu8gGw9BXg6bVDIyP8s6fOiKOnOoXfUymyk5uhQaY7spMKn1suTCyNpaU0FkHIBYmdJIFdwDQqhXBCBUDDQEMgjuoAqRHhONXDs0NprFjB87wQ2cn0SmOR2CGIeEJiJ0kQ+3U4jhMe96SxSOz4wvO84NfJ1LrSOalw0WfNJymNFXu6bU443c2pvSI7JHYIIq4kjNh56qmnwHEc7rrrLgBAS0sLbr/9dowYMQLp6ekYMGAA7rjjDrS3t3u9juO4Hj8rVqyQ4RPIyykfvw6DGgsGZnetK4WVplZgxuhiAKmRzhEiO3pxGiv5RZ4cmCyu7xPHATqNuM8OiR2CiCcJUXq+ZcsWvPzyy6isrBQeq62tRW1tLZ599llUVFTg2LFjWLBgAWpra7Fy5Uqv17/xxhuYNWuW8Ht2dna8lp4wtPo0FGTQMNDAsKjO+SMK0S8rDUBqRDiEfkw6jVAWnQqfWw7M7iGgeo0KHMcJaSzy7BBEfJFd7HR0dODqq6/Gq6++iscff1x4fMyYMXj//feF34cMGYInnngC11xzDex2O1Qqz9Kzs7NRXFwc13UnGr7dkxk0DNQ/PM/jv26xM6eyH2rbugCkRoRD/F1RuHsT0LiI2GAWmZMBkEGZIGRC9jTWwoULMWfOHEyfPj3ktu3t7TAYDF5Ch71Hfn4+zjrrLLz++ushJzhbLBYYjUavn76Ob/dkBg0D9c/uWiOOuVNYU0cWCkbdVIhwnOpwdVDO03sMyqkg8uSAecD0aa5zFqWxCEIeZI3srFixAlu3bsWWLVtCbtvc3IzHHnsMN998s9fjjz76KKZOnQqdTofPPvsMt956Kzo6OnDHHXcEfK8lS5Zg8eLFvV5/IuHbY4dBnh3//FeUwtJpVDCkp0YJts3hFIRNboZWmHaeCiJPDnwjO+kU2SEIWZBN7NTU1ODOO+/EunXrkJaWFnRbo9GIOXPmoKKiAo888ojXcw899JDw79NOOw1msxnPPPNMULGzaNEi3HPPPV7vX1ZWFtkHSRBaRKZTMTQMtCfiKqw5lf0AQBTZSe4IBxO9Cg7ISlcLF+NkF3lywTxReq1L5KRTNRZByIJsaazq6mo0Njbi9NNPh0qlgkqlwoYNG/Diiy9CpVLB4XCdDEwmE2bNmoXMzEysWrUKarU66PtOnDgRJ06cgMViCbiNVquFwWDw+unrtLoNyLk6X88ODQP1xTeFBcBTgp3kF30merN1GigVnCDyum1OWO00nDLaeMSOTxqLIjsEEVdki+xMmzYNO3fu9Hrs+uuvx8iRI/HAAw9AqVTCaDRi5syZ0Gq1+Oijj0JGgABg27ZtyMnJgVarjdXSExI2yZpFchg0DLQnLIU1daQrhQUgZTw7rMcO+14wLwngMinn6VPr7ybWBDQoU2SHIOKKbGInMzMTY8aM8XosIyMDeXl5GDNmDIxGI2bMmIHOzk68/fbbXkbigoICKJVKrF69Gg0NDZg0aRLS0tKwbt06PPnkk7j33nvl+EiywmZf5WV4X6zYMFAn7xoGWmgILRiTGfEsrAvH9hMez3Rf9M1WB+wOJ1TK5BSFp3yq9pQKDnqtCh0WO4zddhI7UcY3skOl5wQhD7KXngdi69at2Lx5MwBg6NChXs8dOXIE5eXlUKvVWLp0Ke6++27wPI+hQ4fiueeew0033STHkmXD4eSFqea+kR02DLTFbEVrpy3lxc6uk0Ycb/FOYQEQmusBrgtUtk86MFkQDwFlGNLcYifJo1pyEDCNRZEdgogrCSV21q9fL/x7ypQpIUvIZ82a5dVMMFVp77IJLel9q7Fcj6nRYrbSMFD4T2EBrpli6WolumwOGLuSV+z4RnYAwJCuRm17d0qMyog3Hd2+aSzXfymyQxDxJTlj9SkGEzGZaSqo/aRfaBioC1cjwVoAwJyxJT2eTwWTcqvfyE5qlN3LgdnqHgLK+uxoXH+fNAiUIOILiZ0kQDwE1B80DNTFrpNG1LR0IU2twPkjC3o8nwomZaEfk+i7wi7Eyfy55aLDPS4iQ0OeHYKQExI7SYC/C5gYaizoYo07quObwmKkQmNBVrXnm8YCUmPie7zpcH+XfNNYVrsTDmfwND1BENGDxE4SIDQUDOAzoWGgPo0E/aSwAJdRF0juxoIeg7Kn6kr43Eks8uSCDQLN9BkXAVB0hyDiCYmdJCDQEFAGGwbalsJprJ0n24OmsIDUiOz4+65kpkD6Ti46fPrsaEV9rqgiiyDiB4mdJKA1lNhhkZ0UFjtrd9cDAKaNLPKbwgKS37PjdPKefkx6cRqLRXaSN6IlF76l5woFJ0R3uimyQxBxg8ROEkCendCcaO0CAJw2IDvgNoJRN0kv+sZum+ATydZ5+goxkWdK4oiWHPA8L3RQZmIH8MzHooosgogfJHaSABaxCRzZoWGg7MISKKoDJH8ai/XYydSqoFV5vCPC505ir5IcWOxO2N3iMkPrOd40H4sg4g+JnSSgNZRBmYaBotPKvBPKgNsk++Rzwa+j9/6eZJJBOSawFBbgKT0HaPI5QcgBiZ0k4FSoNJbPMNBUhEV2xNUwviR7U8FTHf4jgMnuVZILoXuyRgmFghMe90R2klNUE0QiQmInCQhlUGbDQIHUrcjqtEhIYyX5Rd/fXCyA+uzECt9KLIYnspOaNx4EIQckdvo43TYHzO6oRSCxw4aBAp7p6KlGp/suWhcsjZXkF33WQdt3fhrrs2Oy2KnRXRQRzMlpPmJHzQzKyfk9I4hEhMROH6fNLV6UCk64aPmD9dpJ1WGgnshOMM9Oco9NENJYPTw7oonvSSr05MC37JxBpecEEX9I7PRxWPv/HJ0GHMcF3C4nxedjMc9OhoRqrGSNcLS4vyu+aSyNSoE0tetUkKx+JTkIJHaY4KZqLIKIHyR2+jit7gqrQENAGTkpPPnc6eSFC0t6kMhOpigylowRjlOCt0vb4zmafB592KgIX89OGvXZIYi4Q2Knj8N657BeOoFI5caC4jvoYJEdrUqZ1BGOQAZlgHrtxIIOi+s7FCiNRZEdgogfJHb6OC0dPadY+yObNRZMwV477A6a4yCImUBkJnGEI1jVHvXaiT4d7shOoDRWN0V2CCJukNjp47S4DcqhxA6L7KRi6TmretGplUF9TUDyTj7neV6UxvIT2Unysns5EPrs+Kax1JTGIoh4Q2KnjxOqezIjlYeBCg0Fg6SwGMk6MqLT6oDF3VDSr9hJ8rJ7OWCl55lpZFAmCLkhsdPHCTUElJHKnh0hshPEnMxI1ggH+55oVQq/x4HSWNGnw+rpoCyGSs8JIv6Q2OnjtITonsxgBua+3FTwxS8O4IY3t8DmCK/zrGcIqASxI0R2kivCcUpkTvaXykv2uWByECiNRVPPCSL+hI7rEwlNa4iJ54ycPh7ZsTuc+MtXB2G1O7G/3oQx/bMkv9YsoaEgI1kbC7YGGALKYHPBTBTZiRqB0lhUjUUQ8YciO30cIY0VwrPT14eBHm42C+sWT5OWAhu46HuH7Y9k9eycCvE9oT470Sf0bCwSOwQRL0js9GF4npcc2fEaBtrV96I7e2qNwr/NYYodFtkJNvGckazpnEDdkxmZSVqFJicBxQ5Fdggi7pDY6cOYLHbYHK6xBqHEjtcw0D7Ya2dvnUjshHlHzO6gpUV2ktOoG6x7MpC8ES05EdJYFNkhCNkhsdOHYT4MnUYp9O4IRl8eBrqnLvLIjqf0PPQxYk0Fk8270uIeApoXyLOTRqXn0SZkZIfEDkHEDRI7fZhQPgxf+uowUJ7ne5XG6gxQAuyPZG0qGKpqLytJI1pyYbE7hKirvkefHdfvlMYiiPhBYqcPwyI7ge7Wfemrw0CbTBZB2AEeD45UqKmgp5lkILGTKeovxPPJN/E93ogHyfrOY2ORHbuTD7uNAkEQkUFipw8jtRKL0VcbC+4WpbAAT6RGKmZqKhgyssM+t5MP3xNF9ERsilcqvPsapWk8p13qtUMQ8YHETh9GakNBRl8dBipOYQERlJ4zg7KkpoLufjMWO5zO5IlwMM9OoO9KmloBtdJ1UU42v5IcsO+obwoLADRKhSCAqIsyQcQHEjt9GJaaCDey09eGgTJzcr7eVUkUdul5OGksd4SD5z3t/vs6FrsDJvcxC1R6znFc0pbdy4EgdvxUAHIcRyZlgogzJHb6MJF6dvraMFBWdn5meQ6ASErPpRuU09RKaFSuP4tkSWWxVgNKhUfQ+IPmY0UPcxCxA9Dkc4KINyR2+jAsHRV2NVYf8ux0Wu040mwGAJxRngugF00FJYgdIPkaC4q9XQpFz7lYDMGcnSQiT048Zef+v3M0+Zwg4guJnT4M64qbmxH4bl1Mbh8cBrqv3gSeBwoytRiYqwMQQWTHJr2pIJB8jQU93q7g3xPqtRM9gqWxAOq1QxDxhsROH4aJlkBdcX3pi5EdlsKq6GeAzn2XHGmfHSnjIgBxY8HkuOifEkRxiJEiSSby5CRkGosiOwQRV0js9GGk3rEz5B4GarE7cMvb1fjLlwckv4ZVYlWUGIQLR2e4YieMqedA8k0+Z9+TvBCiOFNLaaxowYRyoGiijuZjEURcSRix89RTT4HjONx1113CY93d3Vi4cCHy8vKg1+tx+eWXo6Ghwet1x48fx5w5c6DT6VBYWIj77rsPdrv8d+TtXTa88PkBXP/G9zFp0mZzONHeFZ5nR+5hoBsPncInu+rx/OcHJEcPWCXWqH4G4cIRTuk5z/PoDDuNlVyNBaW2KBDK7pMkoiUnoSI7nvlYdKwJIh4khNjZsmULXn75ZVRWVno9fvfdd2P16tV47733sGHDBtTW1uKyyy4Tnnc4HJgzZw6sViu+++47vPXWW3jzzTfxxz/+Md4foQcqBYdlGw7iq/1N2HmyPerv3+ZOYXEchAGfoZB7GOiOE67jYHfy+N9PzSG3dzh57K83AXClsVgnWrPVIVlAWuxOONz9cpLRoLx2dz02HT4VdBvJYictuUSenLBGlqHFDkV2CCIeyC52Ojo6cPXVV+PVV19FTk6O8Hh7eztee+01PPfcc5g6dSomTJiAN954A9999x02bdoEAPjss8+wZ88evP322xg/fjxmz56Nxx57DEuXLoXVKq8vJUOrwgUVxQCAD7fVRv392ciH7HR1jw6twZBzGOiOE23Cv7/c1xhy+2OnzOi0OpCmVmBQfoZQ2eJw8rBITMOJLyY6iZ6dvuJd+anBhN/8sxrXvfF90LVKFTuZSToXTA5CpbEEg7KNxkUQRDyQFtePIQsXLsScOXMwffp0PP7448Lj1dXVsNlsmD59uvDYyJEjMWDAAGzcuBGTJk3Cxo0bMXbsWBQVFQnbzJw5E7fccgt2796N0047ze8+LRYLLBaL8LvRaPS7XW/5+bgSrN5eizU7avH7C0eFJUpCccrdETdHYvdkhivlZY57Y0Ge57GtxhPhWr+/EU4nH7QUmqWwRhQboFRwwgBFwJUmkDLpnd1ha1QKqJTStH1fGRmxZrtLRHfbnFi3uwGXTyj1u90pyWmsxIjsHG7qwOvfHkGn1QGHk4fd4Zoh5XDysDl5OJxOjO2fjQdmjQDHRe9vKpqYg3RQBsTVWCQsCSIeyCp2VqxYga1bt2LLli09nquvr4dGo0F2drbX40VFRaivrxe2EQsd9jx7LhBLlizB4sWLe7n60Jw3vABZ6Wo0GC3YfOQUzh6SH7X3ZpGdXIkpLIZcjQXr2rvR3GGBUuHqHnvKbMX2E204bUBOwNcI5uR+BgAQXttlc6DT6kCehP2GMyqCkSgX/WDwPI//7qwTfl+9ozag2PEYlKWmseS9AD/x3734IkTk79uDp3DVmWUYlJ8Rp1WFR6jSc+qzQxDxRTaxU1NTgzvvvBPr1q1DWlpaXPe9aNEi3HPPPcLvRqMRZWVlUd+PRqXA7DHFWLGlBh9tq42q2Al3LhZDrmGgLIU1oigT5fk6fLyzHl/tawwqdoSy8xKD8FiG1iV2pJqUWU8enYRREQxDH0jn/NTQgUNNZigVHBxOHt8caEaL2er3+yB8V0J02mYizyRjRKut04qvDzQBAO6YNgyGNBXUSgVUSg4qBQeVQoEXvzyAY6c6cbipI4HFTnBTfFofqcZyOHn89auDmDQkD2e6m3oSRF9ENs9OdXU1Ghsbcfrpp0OlUkGlUmHDhg148cUXoVKpUFRUBKvVira2Nq/XNTQ0oLjY5YUpLi7uUZ3Ffmfb+EOr1cJgMHj9xIqfjy8BAHyyqx4We/RObK0Rip1smRoLshTWuLIsnD+iEADw5f7gd+97hB47mcJj7OIhdfJ5ZxgTzxl9IbLz3x2uFNbUkYWo6GeA3cnj0109o5kOJy+kLCV7dmT83Gt318Pm4DGyOBP3XDAcvz5nMOafXY6rJw7EVWcOwOUTSjGmfxYA4HCTWbZ1hkJqNVaij4v44WgL/rzuJzy2Zo/cSyGIXiGb2Jk2bRp27tyJbdu2CT9nnHEGrr76auHfarUaX3zxhfCa/fv34/jx46iqqgIAVFVVYefOnWhs9Fw0161bB4PBgIqKirh/Jn9MHJSHwkwt2rts+FpCBZJUmA8jXM+O3JGdcaXZmDKiEBwH7DppRIOx2+/2pzosaDBawHEuzw6DRWjYnXMouqzh9dgBEr+TsDiFNWdsP1w8ziWoV2/vaYRv77KBDW8P1aLAMy5Cvs+9ervrc7HP5I8h7mjO4eaOuKwpEqSmsRJ96jlLabb2sXl6BOGLbGInMzMTY8aM8frJyMhAXl4exowZg6ysLNx4442455578NVXX6G6uhrXX389qqqqMGnSJADAjBkzUFFRgWuvvRbbt2/H2rVr8eCDD2LhwoXQaqV1FY41SgUnnLg/8nMxihR28gnlw/BFDs+O08ljp7vsvLI0GwWZWlSWZgMAvgrgzdhb5yo5L8/L8Lpg6N0VWVIbC3omnocjduSPcASDpbA0KgWmjSrERZX9AACbjpxCo494ZCNFWDooGOxzWx1OWS7CzR0WfHfIdUPAPpM/BhfoASR2ZKcjhEG5rwwCZd8DKpEn+jqyl54H4//+7/9w0UUX4fLLL8e5556L4uJifPDBB8LzSqUSa9asgVKpRFVVFa655hrMmzcPjz76qIyr7snP3WJn3Z76sEcdBEI83DEcQo2MONnWhXmvf49HPtrduwWKONxshsliR5pageFFrgvVVJbKCiB29tS5xBEzJzPCbSzomXgehmdHNBAzFg0hewtLYZ03vACZaWqU5epw2oBs8Dy8TMuAp2ovTx9a/GdoVELTSTmE3ic76+DkgXGlWRiYF9iLM0iI7CSm2LHanUKHcn2A752uj/TZYWIn0UUZQYQiocTO+vXr8fzzzwu/p6WlYenSpWhpaYHZbMYHH3zQw4szcOBAfPzxx+js7ERTUxOeffZZqFSyV9R7UVmahfI8HbptTny+tyH0CyQQsUE5iGdn6/FWzP3Lt/j6pyb8Y+PRqF3oWQprTEmWUP49daRL7HxzsNmvl4lVYo0S+XUAj2iRKhrDnXgOeNJYTj78oaOxxjeFxbi40n8qK5zviULBCVE0OVJZq3e4PtdFlYFTWAAwuMAldppMFpgSMPom/m4GmnrOSs8TPY3F+ll1Wh1wOhNP+BOEVBJK7CQrHMfh5+P7A4heg8FIDcqBIjsfbjuJX7yyCc0drrSHk3f1b4kGO0QpLMboEgMKM7XotDrw/ZGWHq9haSxxJRbguXhIFSHCxPMwIjtpagXUSleII9F67exvMHmlsBhzKvuB44Ctx9tQ09IpPC61xw5DLnN2fXs3thx1fQ/mBElhAa5BrQWZrkjVkQSM7rCoY5o6cG+n9D6WxgKA7igWWBBEvCGxEydYKuvrn5qiYg5ukVhh4wsTO2wYqNPJ45m1+3Dnim2w2p2YPsrTtyicGVTB2M7MyWVZwmMKBSdUZX2x1zuV1W1z4GCTy3xa0S/L6zld2JEd98TzMCI7HMcl7OiEj93RD5bCYhQZ0jBxkKs0WJzKapXYY4chlzn7vzvrwPPAGQNzUJKdHnJ7IZWVgL6dUOZkQDQuoo9EdgBPlJQg+iIkduLE0EI9Rpe4SoQ/3lUX+gVBaOu0ClGXcMWOId0zDLSuvQu3/KsaS786BAC4ZcoQvHLtBKEBXzT8RVa7E7vdKalxosgOAJzvTmV9tb/RK2V2oKEDDiePHJ0aRQZvr4kw+VziHTHbLlA6IRCJUJnkS6AUFsNfVVa4VXvCqIw4R7TYmoNVYYkZUsDETuJVZLG/m2CDZ9P7SDWWRbS+RPcXEUQwSOzEERbd6W0qa8vRVgAu74LUSd4MpWgY6C9f2YS1uxugUSrw5yvG4YFZI6FQcBFNFw/ETw0mWO1OZKWrMTBP5/Xcz4blQ63kXA3iROkIwZxcYugxDkDnFi1S1+bpsxPecfI0FkycyE6gFBZj9ph+UCo47K414pBbBEjtnszIlCGiVdPSiW01bVBwwOyxgftjiRmc767ISsA0lklKZKevpLFEkZ1OW+IIf4IIFxI7cYTdtW452oLatq6I32eze8r1xEFSBib0hA0DrW3vRl6GBstvmug1aoCJnWhEdlgKq7I0q4dw0WtVwmcQl6ALfp1+PZs96sNuKhh+nx0gMRsLBkphMXIzNPjZUFeX7jXufjXhGtnlSGOtcX+uSYPzUJgprZt6IqexwonsdNkcCVnxxxBHniiNRfRlSOzEkZLsdJxVngueB9bsiDy6s9lt6J00OLL27cVZrgvKyOJM/GfhZJzh0waepXyicde5vaYNgEvs+INVZYl9O55KrJ5iJyMOTQWBxGssGCqFxfD0dDoJnucjMCjHP6LF/hZCVWGJYRVZR5rNCVclxMROpoTIDs97+2ISDYuoSIHSWERfhsROnGHjIyJtMGjstmF3rSvNE2lk5w8XVuB3s0di5S1noyxX1+N5j6Do/YWeVWL5+nUYTOxsOdoCY7err42/mVjC2rTh+YnY1PP0MNNYmQmWxgqVwmLMGF0EjUqBQ01m7Ks3iQzK0ppsxjuNdaipA7trjVApOMwaIy2FBQBluTqoFBy6bA40mPx34ZYLJpCDRnbUHvGdyCJCXIFlpgntRB+GxE6cuXBsP6gUHHad9PgqwqH6aCucPDAwTydEaMKlosSABecNCegp0EcpjdVpteOnBldKalxZtt9tyvMzMDg/A3b3MMsTrV0wWezQKBUY4u6UKybcFFskU8+BxEtjhUphMQxpapw/ogCAS1BLHQLqeX18++ywdNvkoflhme3VSgUGuIV6oqWyzCGGgAKASqmAxl2WnsgVWd1kUCaSBBI7cSY3Q4Nzhrl8FR9FYFTedIT5dWI3gThaBuVdJ41w8kCRQYsiQ2BhxqqyvtzXKFRuDSvS+x1vIJSeS7zLjGRcBJBYk895nscaCSksBktlraw+AavDXbUnsdO2MPk8DiKP53ms3hFeFZaYwQlakcW+m5kBRkUw0tSu73cim5TFvbYoskP0ZUjsyIA4lRWuOXHzYZdf56wIU1hS8ERPencSFg//DMY0t9hZv79RSNH5MycDIoNymJ6dcJoKAokV2dnfYMJhCSksxtSRhdBplGgyuRpEpquVksWep79Q7C9s+xtMONjYAY1SgRmji0K/wAdhRlaCVWQJaawQ3zkm3BO5/Fzc3ZwiO0RfhsSODFxQUYw0tQJHms3YddIo+XVmix07TzK/TuwiO3qhS3HvLnjbmV8nQAqLcUZ5LvRaFZo7rPhg60kA/v06gMdoLDXqZBZKzyMzKCeC2JGawmLoNCqv5pDhpIfiWXLPUljnjSgQjnc4JGpFljnEEFBGX2gsKI7sJHIEiiBCQWJHBvRaFaa5L0b/2XZS8uuqj7XC4eTRPzvdr7E4WkQrjbVDVHYeDI1KIaT2TrpL8kNFdix2J+yO0FUsQul5mP2IPFVJoY/Bqh9PYOb/fY1jp6J/0Q03hcUQp4XyJPp1gPhFtHqbwgKAwcJA0MRKY3k6KAcX2H1h8rlX6TmlsYg+DIkdmZgr6nbrkFg6uzkOfh0gOgblVrMVx065ZjRV9s8OuT3z7TBGBhA7YtNnqPlYdodn+rROHbvIzj83HsP+BlPU5p6JCTeFxTh3eL7gGQkvshOfkvtdJ404dqoTaWqFkMYMF5bGOtHa5XeYrFx0SOizA/SNyedWO5WeE8kBiR2ZmDKiEFnpajSaLNjkbhIYCubXmRhhfx2phDt/yh873Om2QfkZyNKFTlFMcVcQAUBpTjqy0v2/RqPyDOkMtb5O0V2pLuJxEcHFDs/zONjoiiywkvloEm4Ki6FVKTFrtKuUW2rZOeCJaHVaHbBJiJxFCovqTBtVFHYXcEa+XoNMrQo8D0FYJwJmCR2Ugb4x+ZyaChLJAokdmdCoFMJ051U/hk5ldVkdQjfiSPvrSMXTyybyk9uOEM0EfSnMTMM497aBUlie9UnroszuRJUKTijzlYrYqBvMRN5ksghm3j0xEDtfH2gGAEG4hMOd04dhTmU/XD+5XPJrxBfoWEV3nE4e/3WLuItDTDgPBsdxCVmRJWUQKNBH0ljiyA6NiyD6MCR2ZOSS8f0BAJ/uqg95d/fj8VbYHDyKDNoeM6aijZDG6kWO3jMmIlvya/6fe2SFb0rLF6ldlNkdtk6t7DGqIhQswuFw8kENpCyqA7iiC9GaFA+4okbsIj6mvzTRKKY0R4elvzo9rNeqlAqhJ1GsTMo/1rTiZFsX9FoVpoyILIXFSMSKLCnjIgBRGiuBIzviQaCJLMoIIhQkdmTkjIE56J+djg6LHZ/vbQi67Sb3iIiJg/LCvnCHS28NyjzPC5VY48ukX2ivmTQQ3zxwPn5xZlmI9bnviEOlsQRzcngpLMCVYlC6x8MHMykfaPSOKOyvj150p8VshbHbDo5DzAWuGE+vndjcyTNv04yKIiG6ESmJWJHFjltSpLHEg0ApjUX0YUjsyIhCwWGuu+fOf34Mbm4Vhn/G2K8D9N6gXG/sRpPJAqWCQ0U/6WKH4ziU5uhCijmdxHEWniGg4XtCOI7zlGEHMSkf9BE7e9xDTKPBEXe0oiQrvdeiIBxiWXZvdziFFBbrN9UbEi2NZXc4hVlXIcWOhqWxEjM9ZHM4vYonaOo50ZchsSMzl57mSmWt398ozDHypdvmwI9uD0ys/TpA75sKsuGfw4syw+5cLAXP5PPg6+uMsMcOQ4pJ+UCjS9z0c4/uiKZJmaVm2AU9XsRyLti3h07hlNmKvAwNJrsntPeGwfmuNNaRBEljif9mQqWxhD471sQcBOo7oJQiO0RfhsSOzAwrykRFPwPsTs9Ua1+217TBanciX6/FkDhc+DJETQXD7fAMIKIUVjhIbSzYGeHEc4aUCAeL7FzkNtqyie3RgF3AWaomXsQyjfWhu6/UnMp+fseBhEt5viu919ppC3izEE9MFtd3RaNSQKMK/vlYGitRjb++6TXy7BB9GRI7CQCL7nwYoMHg94JfJzfmfh3AEznh+chOcDsiMCeHg15iNVZv0lhA6MaCrWYrmjtcF9iLKl0pmf31Jsl9k0JxpEkmsSMhfRcJ3TYH1u6qBwD8PMJGgr7oNCqUuKNqidBckEV2QqWwgMTvs9NT7CSmKCMIKZDYSQAuHlcCjgO2HG1FTUvPfiGbj8Snvw4jXa0E01Th+nacTh47alyRHall5+HiMVDHOI0VIrJz0O0T6Z+djtElBmhVCnTZHFHrpCxXZIf184l2GuuLvY0wWx3on52O0wfkRO19hYqsBDApd7gjO1LETpo6sauxeqSxElSUEYQUSOwkAMVZaTh7iMuL89F2b6OyzeFE9bFWAPHx6wAuc26GMF08vBPckVNmmCx2pKkVGF6UGYvlCdVVIZsK9jayE+Kiz1JYQwv1UCkVGFns+rx7o2BSdjp5HHGLJuZLiRdCRCvKaayPtrsilz8fXwKFInoRSqEiKwF8O0yAS2mUmJ7gfXZYZEfrTsfZnbxXR2WC6EuQ2EkQ5rp77qz68aSXT2bHiXZ02RzI0akxrDB+F70MiYLCF5bCGl2SFRVPhj/0mnDTWJEalINf9A80eMQOAIxyN0OMhkm5tr0LVrsTaiWH/jnpvX6/cIhFNVZ7lw1f7WsCAKECMVokUkUW+3vJDCONlail52wIqHjcCKWyiL4KiZ0EYdaYYmhVChxs7MBukcmVzcM6a1BuVO+GQxFpr53tMU5hAZ6hniHTWJbopLFMIdJYw2IgdlgKa2BehtDvJ154qtCid2Fbu6seVocTw4v0GFkcvEN2uLA0ViJUZHV0s4aCob9zaQneVJDNG9NrVUIH8kSNQhFEKEjsJAiGNDWmV7gnoYvGRwjzsOKUwmJE2mtnp3sm1rgYmZMBzzTpkE0Fbb1LY3lKsP3v52CDK13lG9mJxtgIufw6gOhzRzGy86E7hcUimNGETT8/eqozaubwSJE6BBRI/DSWxR3Z0aoVCd8TiCBCQWIngWDjIz5yT0K3O5z44Wh8zcmMDImN+3ypb+8GAAyIYcdfyU0FexvZSQ+czumw2FHr/qxM7Izs5/Ls1LV3o62zd2XQzGwb7x47QGivUrg0Grux8ZArQhmtKiwxJdnp0KgUsNqdONnaFfX3DwchjZUWRhorQcUOS6+lqZTCCJFEFWYEEQoSOwnEecMLkK1zTULfeOgUdtcaYbY6YEhTRT30H4pIGwuylI8hjAnd4SK9qWDk4yKA4Bf9Q25zcr5ei2ydRti+LNflr+ltdIdFdgbLENmJdp+dNTvq4OSB0wdkoyw3+iJYqeAwKI+ZlOX17QiRHQnRxPQ+Uo2VplaKIjuJuVaCCAWJnQRCo1JgzlhXc7r/bDsp+HXOLM+Nu29DH4FB2enkYXJvb5BwZxspGRJTbL03KHsmn/vCZmL5msZHFTPfTu8qsthFe1CcK7GA6PfZ+dBdYRiLqA4jUWZkhZXGSnABIa7G0kksCiCIRIXEToLBGgx+uqseG35yVa/EO4UFRGZQdnVcdv2bCYVYIL2DMktj9bapYM+LPis7H1bkI3aiYFK22B044U7HyOPZcf2/67DY4eylB+Zosxnba9qg4IA5lbETO0JFVoJEdqSksVhkx2J39vo4xwIhjaVWCn9ziSrMCCIUJHYSjAkDc1Ca45qE/u1B9/DPOJuTAY/YCedOjkVANEqF0JsjFoSdxopCU0HfsRkHG73NyQzBpNyLsRHHT3WC513ly/l6TegXRBl2oeZ5CJG6SGF9oyYPzUdBprbXawtEolRkmSOI7ACJmcpiaSytWuEROzQfi+ijkNhJMDiOE4zKgOvCProkvn4dQGxQln5yYxGQzDRVTMda6CTO7ur9uAiX2LE5eKHnCEPcUFAM+391sLEDNkdkDdhYc7xBBRlxGQ/iS5paKYjVQGX3UuB5XhiBEosqLDGJlsaS1EFZldhih33n09RKod0DpbGIvgqJnQTkktM84f4JA3OgilFzvmBE0lSQGVpjmcICvGd3BbtI9Dayk6FRglmlxP6VbpsDx91jPXzFTmlOOjK1KlgdThyKsMmdnGXnjGj02tlTZ8ShJjM0KgVmji6K1tL8wgbk1rV3y3pBDkfsKBQc0tSuv+1EnI/VbRd5dliZfAKKMoKQAomdBGRoYSbG9HdFCCYNjn8KC4iszw6L7MTSnAx4z+4K5tvp7WwsjuNElUkesXOk2QwnD2Slq1Gg1/Z4DStBj9S3I9cAUDHR6LXz0TZXCmvayELBBxQrsnUa5Ohc+5AzlWUOY1wEkNgVWRZxZIfSWEQfh8ROgrLk0kpcd3Y5rpk0QJb9R2JQZhfGWF/YxLO7Ap18nU5euIBEmsYCPL6ddlGE44AoheUvzeQxKUdWkZUQkZ1e9tpxOnnBrxPt8RCBSISBoCy6KSWyA3i+m4kc2UlTidNYibdOgpBCbG/BiYgZW5qFsTEcuRAKIbITRkrAk8aK/ddKp1Giw2IPKMa67Q6hMizSyA7gP8JxMEDZOaO3FVmHhR478S87Z/S2186Woy2oa+9GplaFKSMKo7m0gAzOz0D1sVaZIzvhiR0hjZWAkR2h9FytENK55Nkh+iqyRnaWLVuGyspKGAwGGAwGVFVV4ZNPPgEAHD16FBzH+f157733hPfw9/yKFSvk+khJQyRNBQWDsja2kR0gdJpNfAfKUgWR4C/CEagSi1EhqsgKZqD2h7HbhuYOCwCgPD92XahD0dteOyyqM3NMMdJ6cfzDYZDMA0EdomiiXmIqN5EjO0JTQZWCIjtEn0fWyE5paSmeeuopDBs2DDzP46233sLcuXPx448/YuTIkairq/Pa/pVXXsEzzzyD2bNnez3+xhtvYNasWcLv2dnZ8Vh+UsMMyuGksYSGgnGI7GSEOPmy9Fa6WtmrAar+Jp8HqsRijCjOhIIDTpmtaDJZUGhIk7y/o+6oREGmNubpwGBkpvXOoLxuTwOA2DYS9IVFwg7LFNkR/61IGQQKJLpnx9Nnh0l2EjtEX0VWsXPxxRd7/f7EE09g2bJl2LRpE0aPHo3i4mKv51etWoUrr7wSer33RSY7O7vHtkTvYJ6YyAzKsb9Ih2os2GmTPn06GL6RHZvDKaRJhhVl+n1NmlqJQfkZONRkxp46Y1hiJxH8OoBH5EVSet7cYUGjyQKOA84oz4n20gLCKrKONJnB83zcy/bZ34payUGrkva9EyafJ6CI6BYNAlW4jyWlsYi+SsIYlB0OB1asWAGz2Yyqqqoez1dXV2Pbtm248cYbezy3cOFC5Ofn46yzzsLrr78eMnVgsVhgNBq9fghvxJETqd1dPQbl2GtofYi+Hyz9lt4Lvw7QcxjosVOdsDl46DRKlGQFFjGRmpSFAaByi500788dDj/Vuz7zgFxdr8zh4TIgTwcF54owNrlTgfEknLJzRiKXdFvEBmUNpbGIvo3sYmfnzp3Q6/XQarVYsGABVq1ahYqKih7bvfbaaxg1ahTOPvtsr8cfffRRvPvuu1i3bh0uv/xy3HrrrXjppZeC7nPJkiXIysoSfsrKyqL6mZIB8Qlb6ok4Xn12AAgegkBND9mdspSBjMEw+KRzDoaoxGJEalJOmMgO8+xEkMba5xY7IwJEvmKFVqVEaY7L5yRHRVY4c7EY6Qk8+bzbX+l5HCM7VrsTBxtNYfveCMIfsoudESNGYNu2bdi8eTNuueUWzJ8/H3v27PHapqurC8uXL/cb1XnooYcwefJknHbaaXjggQdw//3345lnngm6z0WLFqG9vV34qampiepnSgbSRBUYUlNZ8UxjsUGlnQENyq7Hex/Z8TbqCubkguCVUhXuTsrhTj9PGLGTHnlkZ79b7Iwsjq/YATwzsuSoyAq3EguAYN4OFTE52mzG524fVLzwHgQa/9lYj/93D6Y/9zW+Odgct30SyYvsYkej0WDo0KGYMGEClixZgnHjxuGFF17w2mblypXo7OzEvHnzQr7fxIkTceLECVgsgcPYWq1WqABjP4Q3HMeF3WuHmXjjkcZiYfWOAHeave2ezGDCjUWthMhOUQix447sHG7qEC4aoeB5XrhIs4u2XPh+7nDY1+ASO8NlEDuesRHxr8jqCLPHDuD5foYyKN+x4kf8+h8/YNfJ9sgXGCae2VjypLHY39qBBnmHuxLJgexixxen09lDqLz22mv4+c9/joKCgpCv37ZtG3JycqDVxm7oYKoQbhdlZmaNRxpL8BQFSGP1di4WwzM2wfXZhIaCISI7hZla5GZo4OSBnxqk+XaaOizosNih4ICyXPnKzgFPRKs9zKaCTiePAw1yRnbkaywYURrLHdkJJogdTl5Ih7IxJfHAM/VcIUsaix3PtggbWxKEGFmrsRYtWoTZs2djwIABMJlMWL58OdavX4+1a9cK2xw8eBBff/01Pv744x6vX716NRoaGjBp0iSkpaVh3bp1ePLJJ3HvvffG82MkLWFHdrri59nRh5jd1dtREQxxU0GnkxfmXQWqxGJwHIdR/TLx7cFT2FtnRGVpdsh9sTERpTk6ydU8saLIXUFW29aFbptDcq+cE61d6LQ6oFEqUJ4X/+hU/2zXuhtNMhqUw4hspksQESdbu2BzuHwrzXE0XnvSWB7PTrfNCYeTh7IX7Rykwo5npF28CUKMrGKnsbER8+bNQ11dHbKyslBZWYm1a9figgsuELZ5/fXXUVpaihkzZvR4vVqtxtKlS3H33XeD53kMHToUzz33HG666aZ4foykJZzGgt02B6zuKd9xTWOFaCrY68iOyKB8sq0L3TYnNCoFynLSQ752VLHBLXakRXYSxa8DAP2z05GboUGL2Yp99SaML8uW9Lp99a4IxJBCvSwDbHN0GgBAa6c17vsWPDthfOc8fXacAbc51OxJ4zR3xO9zCU0F1Qqvv6MumyOsVF2ksLRguNFFgvCHrGLntddeC7nNk08+iSeffNLvc7NmzfJqJkhEl1DREzHMyMpx4Z3sI0UfoqmgOUqRHbFBmXkIBudnSLqQh2tSThS/DuCKTFWWZmH9/ibsONEmWezIaU4GRGLHHH+x0xHmEFDAE9kJ1mfniCglF6/IDs/zIrGjRJpaAY4DeN4VhYqH2GHnHRI7RDRIOM8OkTiwuzkp87FYCkuvVfWqY7FUQjUV9JSeR6fPjtXuFMyhgTon+yIuP5dSPuuZiSW/2AGAyv6u2Ww7Tkg3xe53+3VGyCV2Mlxix2x1SDaGR4sOi+uiHE4ay2NQDvw3Jq4sa45Teo4JHcBVjcVxnKcnUBwmnzudPMzuv2ESO0Q0ILFDBCQcg7JgTo7TiAPpTQV7dweq16jA2ulUH28FIF3sDCnQQ63kYOq240RrV8jtPWks+QaAimE+ox0n2iS/hkV25BI7hjSV4Cdp64zvRZJ95/RhdO1mXqhgkZ3DojTWqThFrCyitBpbYzznY4lvsEjsENGAxA4REM98rNAnN2McGwoCnhNvID9RV5TGRSgUHDLd+/rxeBsAYFihtAu5RqXAUPe2oZoLOpw8jp1yi50ESGMBQGWpK7JzsLFDkuC12B1CdCreDQUZHMfJ5tvxdFCW/jcgxbMjRxqr2909WangoHanbKVEoaKFOGJLYoeIBiR2iIBkhBHZESaex8GcDIj8RCH67PRm4jmDCTh20pUa2QGAUf2Y2AluUmYVN1qVAv3CmKUVSwoNaSg2pMHJA7trQ/uODjeZ4XDyyExToV+QURqxJkfn+v8Vb98OM9SGI7AFARHwe2xHbXu38Hu80ljihoIM9rckpWCht5hJ7BBRhsQOERB9GMNAhVERcUpj6UKsjfkKojGbSfyZlAoO5fnSe+BUSBwbwVIVg/Iz4uJ5ksrYUubbaQu5rdicHO8hnGKYb6clzpGdU2aXEGGRJSmkhZh6frS5072d61RttjriMjRUbE5mZMQxjSVuZmm1O+PuvyKSDxI7REDC6bNjFBoKxieyw9Zmc/DCwEIxbOq5rpdpLMD7Mw3MC68HDhM7oSqyEqnsXMy4Uukm5X0y+3UYuTJVZNW1uSIwJdnSo1rpIcYwsO/FqH4GaNxRlniksoSGgqLITjwbC/qecyi6Q/QWEjtEQBLZoCyusvJXHSJEdqKQxsoUfaZQnZN9YRVZx1s6sb2mLeB2iSp2xrpNyjsljCnY7+6xI5dfh5GT4U5jxdGgbOq2weT+O+mXFboHE8PTrM+/2GFjLwbn65HvjljFR+x4RkUw4jkfy/ecQ2KH6C0kdoiAhNNUUOieHCfPjkqpEPwE/iJP7IQcTs+TQIgF3LAQM7F8ycnQ4NLT+gMAfr9qJ+wO/0ZUNt4g0cQOKz8/0mwOecH5yT3DaESxvLPmWBqpJY6RnXq3r8aQpopoXITNwcPm57sh7r2Un+kagXMqDo0FWbRU6xXZcX2ueKTRfGeykdgheguJHSIgOqEaK5w0VnwiO0DwxoLmKE09B7zTWOGYkxm/v3AUDGkq7K414q2Nx/xuk0gNBcXkZGhQluuKVAQbQmnstuFkm6u8Xu7ITm5G/KuxmIk4nKgO4P399OfbEfdeyte7xE48Iztizw5bq5S+W72lR2Qnzm0EiOSDxA4RkFC9bMSY4jjxnBFIjPE8L2oqGOXIjsSyczEFmVr8bvYoAMBzn+1HXbt3z51um0MQConSY0cM67ezPYhJ+Se3X6dfVhqydPETvP6QI7JT5/7/1y8Mvw4AaJQKMD96t49o53leSGMNKshAnlvExaPXjr9qrAwJ3Z6jBXl2iGhDYocISIYwf0pKGiu+nh3Asz5fMWZ1OGF3ujoWRyey4/lMkUZefnFmGSYMzIHZ6sAjH+32eu6ou79OVrpaKJtOJFgqa2cQkzLrnDxc5qgO4PHsxLOpYKSRHY7jRL12vP/OWsxWGLvt4DigPM+TxmqKQ/m5v2qs9DA6qvcW33MOiR2it5DYIQISjkGZpbEy4yl2AqxPfOfZ29lYgMeHVJqTHnEpu0LB4YlLx0Cl4LB2dwM+39MgPHdE5NeRs2Q7EJ5OykHEjswzscTIGdkpiaC/ULrGfzqWpTZLstKRplYKkZ24VmOpe0Z24mFQZqM3GCR2iN5CYocICGuO1mVzwOEMPttJ6LMTp9JzQFwa733yZSdjjVIhdH/tDaP6GcBxQNXgvF69z8hiA248ZxAA4OGPdgsRqUSbieXLmP6uz3+yrSvghTZRys4BeTw79UZ3ZCc7vMgOAKRrXN9R38gOM62zaGJBHA3KnjSWn2qsuDQVdP8Nu9NoJHaI3hLRleDRRx9FZ2dnj8e7urrw6KOP9npRRGIgrioJFbqWI43Fuij7prE6o2hOBoAx/bOw+ffT8NTllb1+rzunDUP/7HScbOvC858fAJC4ZeeMzDS1IMT8pbJ4npd9JpYY1lSwM47DQGt7E9lxp4p8PTuHfb4X8TQoe9JYog7KLAIVh2PKbp76u8UjiR2it0QkdhYvXoyOjo4ej3d2dmLx4sW9XhSRGGhVCqjc7slgqSy7wylMKI6rQVnjv+lhZ5QmnospzEwTBkz2Bp1GhccuGQ0AeO2bI9hTa/SInQSrxBITLJXVaLKgvcsGpYLDkDD7EMWCTK1K+N7Gw7fD8zzq3J6d4iimsTw9dlzfizx9/NJYFiGNJeqgHGK0RTRh5xvWoJHEDtFbIhI7PM/79RZs374dubm5vV4UkRhwHCdpPpZYbMTTsyNUi/mE1T0Tz6MndqLJ1JFFuHBsMRxOHr9ftdNTcZOgkR3AMxTU39gIlsIqz9N5XRzlguM4ZMfRt2PssgtCJVyDMgCkq/2nsTwi2CUgWWSntdMWsF9TtOh2R3a8ZmOx0vM4pLHYOYUiO0S0COs2PCcnBxzHgeM4DB8+3EvwOBwOdHR0YMGCBVFfJCEfeq0K7V22oBVZrKFgulop5NjjAfMQ+EZ2PBPP4xdlCpeHLx6Nr39qxjZRV+XyvD4gdk6297jZYZ2TR8rcTFBMboYazR2WuPh2at2tBHJ06ogEtlCNJYrsOJw8jp1yWQVYZCdHp4GCA5y8S8QVxnBgrN/IjvvvKdAcr2hiFsSOaw4diR2it4R1NXj++efB8zxuuOEGLF68GFlZWcJzGo0G5eXlqKqqivoiCfnwmBIDR3Y8lVjxFRcZAfoACZGdBIgyBKLIkIZ7ZwzHI6v3AACKDWkJLc4q+mVBqeDQZLKg3tjtFcHYX886J8vv12HEsyKL9U2KJKoDiDoTi0TEydYuWB1OaFQKlLijG0oFh9wMDZo7rGjqsMRU7PhtKihMPY99GouN3uifQ5EdIjqEdXadP38+AGDQoEGYPHkyVKrEPTkT0UHKMFA5uicDHg+Bb1i9K4qjImLJtVXl+ODHk9hxoj2hU1iAK4UxrFCPffUm7DjR7i12GlyRnUToscNgYqctDpEd5tcJZwCoGH+Tzw83uwRkeZ7OyyuWr9eiucMa84qsbr/jIuLXVJA8O0S0iSjnkJmZib179wq/f/jhh7jkkkvw+9//HlZrfCcNE7FF6LUTxJTI0lhyRXZ81xbNURGxRKng8OwV4zBhYA7mVQ2UezkhGSeYlNuExxxOHgfcM7ESoccOg1VktZhjf5Fk084jj+z07F8TqEIvXhVZFj+DQIVIqs0Bng/eiqI3OJy8cCxK3Wksq90Zt8o6IjmJSOz85je/wU8//QQAOHz4MK666irodDq89957uP/++6O6QEJeMoSRDIFPNPGeeM4IZJ6ORTVWrBhelIn3bzkbs8f2k3spIRkrmJQ9FVlHT5lhsTuRrlZiQK5OrqX1IFeYfB4/z04klViAR5SLL+aeHjve1W2sIitekZ00PwZlh5MXStNjgfjmpdCgFSJbFN0hekNEYuenn37C+PHjAQDvvfcezjvvPCxfvhxvvvkm3n///Wiuj5AZKdVYRqGhoDxix1eIsTB7pN2OCf8wk/JOt0kZ8MzEGl6khyIKpfnRIq6enbYopbESKLIjNBUURXZ0on/HMpXV4T6fqJUc0tRKoYM5iR2iN0Rceu50upT9559/jgsvvBAAUFZWhubm5uitjpAdKSMjTDIZlAM1Fewraay+xojiTGiUCrR12lDT4opm7BPETuKksACP2IlHZKf3BuXAaSzfrtosstMU6zQWayooiuyolAqh2jKW87HYuYade7LcN1EkdojeEJHYOeOMM/D444/jn//8JzZs2IA5c+YAAI4cOYKioqKoLpCQF0kGZbdnJ95pLBa5CTQbqy+ksfoSWpUSI/u5RA2bgJ5InZPFxGtkhLihYEmEYkfooOyOpnRZHTjp7sjsm8ZikZ2Yp7H8VGMB8Zl8ziqxMnzFThwHuxLJR0Ri5/nnn8fWrVtx22234Q9/+AOGDh0KAFi5ciXOPvvsqC6QkBcpkR1PNVa8IztsbT5NBa2sqSClsaKNOJUFeKadJ1KPHcBjUG6NsUG5tdMmREGKsrQRvYfv1POjp1xRnax0NXJ03jcQBXEzKPesxgJENxhxSGOxv28DRXaIKBDR1aCyshI7d+7s8fgzzzwDpZLuppMJFmIPdnIzyTDxHBCVwroHlTIjI2tnT5Gd6FPZPxvAcWyvaUO3zSFcmIcXyz8mQkxunDw7bCZWvl7jNTQzHNI13ulYZk4elJ/Ro1N9vEZGeGZjeX8m37XGAnZjxdLilMYiokGvbn2rq6uFEvSKigqcfvrpUVkUkThIMigLaSx5Ss8B18mXia1EHxfRl6ksc0V2dp1sx/56E3jelTJiEYdEIdtdjdVlcw0DjdUYC5bCitSvA4gjOy6BccTdY2ewn1lp4jRWoLE90aDbTwdlQOY0FokdohdEdHVqbGzEVVddhQ0bNiA7OxsA0NbWhvPPPx8rVqxAQUFBNNdIyEh4aaz4Rna0KgWUCg4OJw+zxSGIHTaVOYPSWFFnaIEeaWoFzFYHPtlVDwAYUZQZs4tupLBhoHYnj9ZOa6/ESDA85uTIuxnrfAZsHg5gTgY8kR27k0d7l02YARZtugOksdIlRHp7CxmUiVgQkWfn9ttvR0dHB3bv3o2Wlha0tLRg165dMBqNuOOOO6K9RkJGApV3izF1yxPZ4ThOuNMUG6jZaAsdRXaijkqpwJgSV3Tng60nACSeORlwfTc8jQVjl8rydE+OXEylabw9O540Vs/UoFalFNI7zTE0KQdKY7EbiFhOPvf17DCxYySxQ/SCiMTOp59+ir/+9a8YNWqU8FhFRQWWLl2KTz75JGqLI+SHlXdLiuzE2bMDiCafi06+rIRXl+DjIvoqrLlgo8nlG0lEsQNAMPfG0qRc19b7yI5nEKgTPM/jcFPgNBYQe5Oy3eGE3enqo5SmDhDZieHk8w6rf7HTRmKH6AURiR2n0wm1uueFTa1WC/13iOQglGeH53khshNvgzLgETTiyA67Q6bITmxgYyMYiSt2Yl9+Xss8O72I7IjTWK2dNqFJZ3mef7ET68aC3aLuyL6ma51PFCoWsMgOO/dk6yiNRfSeiMTO1KlTceedd6K2tlZ47OTJk7j77rsxbdq0qC2OkB8Wtg7UZ6fT6qqEAuJfeg6I5vWI7jSZMEvkqed9GRbZYSRaQ0FGPHrtRMOzIy49Z1Gdkqy0gAb7WI+MEI+tCFR6Hs9qLCo9J6JBRGLnL3/5C4xGI8rLyzFkyBAMGTIEgwYNgtFoxEsvvRTtNRIywkLJFrsTdkfPqB1LYakUnCziQph87j75iuf2JPrU877KoLwMZLqPbVluuvAdSTRi7dlxOnnUC9VYkYsd5tlx8p6O1L7NBMXEOrLD/n40KkWPESC6eKSxqBqLiAERnaXKysqwdetWfP7559i3bx8AYNSoUZg+fXpUF0fIj1gwmC0OZOm89bEnhaWSpSInw6exoPiOk9JYsUGh4DCmfxY2Hj6FEQka1QHEnp3YiJ1mswU2Bw+OA4oMvY/sAMDuWiOAnjOxxMS6106gSizA8/cW09lYVI1FxICwIjtffvklKioqYDQawXEcLrjgAtx+++24/fbbceaZZ2L06NH43//+F6u1EjKgUSmgVrpEjL95OKxCIt5l5wwhsuM+QbKTsILzf7ImosPPhuUDACYOypN5JYHxeHZic5FkUZ3CTC3Uysi/a2ql529sT62rM3UwseOJ7MQ2jeWvNxETZrGcjRVI7FjtTq8UG0GEQ1h/oc8//zxuuukmGAw9W8NnZWXhN7/5DZ577jnJ77ds2TJUVlbCYDDAYDCgqqrKq5prypQp4DjO62fBggVe73H8+HHMmTMHOp0OhYWFuO+++2C3x+4PMRUJZlL2lJ3LJHZ8DMpm0cTzROv9kkzcfO5gLL9pIq6bXC73UgISa89ObVvvGwoymLDwpLGkiJ3YprF8K7EAsZk6ln12XO+td3t29FqV0B2dojtEpIQldrZv345Zs2YFfH7GjBmorq6W/H6lpaV46qmnUF1djR9++AFTp07F3LlzsXv3bmGbm266CXV1dcLP008/LTzncDgwZ84cWK1WfPfdd3jrrbfw5ptv4o9//GM4H4sIQTCTslGmiecM39Jz9l9KYcUWtVKBs4fk9yqiEWti7dlh5uSS7MhTWAz2fWVCY7CfHjuM/LilsXr+DemEv7cYdlBm1Vju8w7HcUIPLxI7RKSEdaZqaGjwW3LOUKlUaGpqkvx+F198MS688EIMGzYMw4cPxxNPPAG9Xo9NmzYJ2+h0OhQXFws/4qjSZ599hj179uDtt9/G+PHjMXv2bDz22GNYunQprNbYzsRJJQIN3AREaSyZIjs6QYgxzw6VnRMuhDRWzMSOK7JTbOh9ZEfs29EoFeifE/g9Yz353GILEtlRx382FkC+HaL3hCV2+vfvj127dgV8fseOHejXr19EC3E4HFixYgXMZjOqqqqEx//1r38hPz8fY8aMwaJFi9DZ2Sk8t3HjRowdOxZFRUXCYzNnzoTRaPSKDhG9I0Pbs0sxg/UEkaPsHPCszRPZoYnnhIvcGHt22BDQaER2xN/XgXk6IW3jj/xMl9jptDpiIjosdrdnx29kh/29xSayY3c4hR4+4uIIQezE6P8lkfyEdUW48MIL8dBDD2HWrFlIS/P+A+/q6sLDDz+Miy66KKwF7Ny5E1VVVeju7oZer8eqVatQUVEBAPjVr36FgQMHoqSkBDt27MADDzyA/fv344MPPgAA1NfXewkdAMLv9fX1AfdpsVhgsXhCwEajMaw1pxrBPDtGmSaeM3zXxkZF0MRzIkc0DLTL6oj6YNj6KAwBZaSLoijBzMmA67utVSlgsTtxqsMKXW50hX23O7Kj9evZiW0aSzxzi93IANRrh+g9Yf2VPPjgg/jggw8wfPhw3HbbbRgxYgQAYN++fVi6dCkcDgf+8Ic/hLWAESNGYNu2bWhvb8fKlSsxf/58bNiwARUVFbj55puF7caOHYt+/fph2rRpOHToEIYMGRLWfsQsWbIEixcvjvj1qYaQxvJbjZUYBmWzTxqLJp4Teq0KaiUHm8M1DDRdE91hoMLE86hEdjzf10FBzMmAy8OSr9fiZFsXmjosKMvV9Xr/YoRqLD+RHXYTEas0Fosea5QKL88QpbGI3hJWGquoqAjfffedkFK69NJLcemll+L3v/89xowZg2+++aZHpCUUGo0GQ4cOxYQJE7BkyRKMGzcOL7zwgt9tJ06cCAA4ePAgAKC4uBgNDQ1e27Dfi4uLA+5z0aJFaG9vF35qamrCWnOq4VvxJMYks0HZt6kgTTwnGBzHCVPBo21Sdjh51BvdQ0CjEtnxfF+HBDEnMwSTsin6JuVAQ0CB2E89Fyae+5xPSOwQvSXsK8LAgQPx8ccfo7W1FQcPHgTP8xg2bBhycnKisiCn0+mVYhKzbds2ABB8QVVVVXjiiSfQ2NiIwsJCAMC6detgMBiEVJg/tFottFptVNabCgh3c/4MyoJnR+7IjncaiwzKBODy7TSZLGiLstejyWSBw8lDqeBQkNn7c0k4kR1AZFKOgfk6WFNBlsay2p3C548mQiWW1vvvl8QO0Vsivv3NycnBmWee2audL1q0CLNnz8aAAQNgMpmwfPlyrF+/HmvXrsWhQ4ewfPlyXHjhhcjLy8OOHTtw991349xzz0VlZSUAV6l7RUUFrr32Wjz99NOor6/Hgw8+iIULF5KYiSJSIjsG2SI7/tNYOi2JHcLj22mJcq+dWnfZeVGmNioX/HA8O4Co104MIjsez44fg7JIlHVa7VH36nkaCnq/LxM7RhI7RITIGutvbGzEvHnzUFdXh6ysLFRWVmLt2rW44IILUFNTg88//xzPP/88zGYzysrKcPnll+PBBx8UXq9UKrFmzRrccsstqKqqQkZGBubPn49HH31Uxk+VfAQ1KHfJbVD27qDs6bNDaSxC1FgwyhGQurbeTzsXw76vhjQV8txrDkYsR0YI1Vh+DMpalQIKzjXHq9PqiPrfvZDGosgOEWVkvSK89tprAZ8rKyvDhg0bQr4HS6sRsSOoQVnm0nPx2niepz47hBex8uxEY9q5GOaPGVSgl9T5W4jsxCSN5Y7s+DEocxyHDI0KJos9JhVZHd3eoyIYJHaI3pK47U+JhMGTxup5cvOksWRqKuhem5N3naRJ7BBiWK+dtiinsVglVkmUIjvsZmGIBL8O4Om1E5M0VpDIDiAyKfuJ9PYWIY3lcz7Jcg91bSOxQ0QIxfqJkOi1/k9uFrtDuAuUTeyIfAVmq11IY1FTQQIQjYyIskE52pGdy04rRX17N+ZVDZS0fX5GDNNYtsDVWIBoPlYMhnJ2UBqLiBF0RSBCEsizwyongJ6lovFCoeCg0yjRaXXALAqtU1NBAgBy3BGBaHt2PENAoyN2irPS8OjcMZK3Z5GdmFRjCR2U/Ud2YtlY0GyhNBYRGyiNRYQkUDWWSZRfj3YJajiIGwtSGosQE6thoJ7ITnQbFUqFeXbaOm2wOZxRfW8LKz0PEdnpjEEay8Q6oAcQO1a7UyiNJ4hwILFDhEQfILLjGQIqb4BQ3FjQI3YoaEnExrNjczjR6PbKRKN7ciRkp6uFG4xoCzlPU8EAkZ0YTj4PFNkR31BRdIeIBBI7REh0AbqmmmRuKMgQR548pecU2SE8pefR7LPTaLKA5wG1kkN+hjz9vBQKTvhsTVE2KQcbFwHEdvJ5oGosjuOEmyoSO0QkkNghQsJOPFa70ytkbpR5VASDiZ1OrzQWRXYIINvt2em2OdEVpUhEnXvaeXFWGhQypm/zYmRSDjYIFBClsWJReh5gXARAvh2id5DYIUIizp+LU1meNJbMkR1RKSyNiyDEsGGgQPSiO7VRnHbeG9iYilMd0U5jhYjsaGMvdnw9O4BI7ES5so5IDUjsECFRKxXQuCszxCblhExj2WhcBOGB4zjk6KLbRZlFdqJViRUpQmPBmEV2AhmUmWcn+mksdjOV6UfsGCiyQ/QCEjuEJPSiiidGwqSx3CffFrMVPO96jNJYBEMYGRGlyE5dgkR2YpfGCjwIFIhPGitoZIfEDhEBJHYISbAZVB2JmMZynxjFRs30AHelROrBfDvRqlpiZeclMlViMfJjlsaS1lQwpp4dEjtElCGxQ0jCM13cXxpLboOy6+Tb5L7DTVMrZO37QyQW0R4GmiiRHZbGaopRZCfwuIjYpLHsDqeQQiOxQ0QbEjuEJPz12vGksRIrskMpLEKM4NmJkrE12t2TI8Uz+Tx6kR2e54XIjr9BoICnIEBKZGdl9Qm8+vVhSfsWp8iDpbGMJHaICKCrAiEJnTBdXOzZcUd25BY77pOvR+xQCovwEE3PjsXuEDwy0RoCGikFepbGil5khwkdIEhTQYlix+ZwYtEHO2Bz8Lh4XAmKQ4hDk8UlYjQqT0GEGIrsEL2BIjuEJPwNA2V3WLIblN1CjF2ESOwQYrJ10RsZ0dDu+o5pVQph7pZcsDTWKbMVTicflfdkQ0CBYJ4daR2U69q6YXO41lVv7A65bxbZ8VeJBZDYIXoHiR1CEsyzk8il53b3CZ/SWISY3Az3MNAoRHbE0845Tl5fGItYOZx81AQAGwKq4ABVAN+bJ7IT3LNT09op/LtRgtjpcEd2/KWwACBLR2KHiBwSO4Qk/E0+Z54d+Wdjee+fIjuEGE+fnd5fJBPFnAy40j0s2hGt8nMW2UlTKwOKOamRnRMisSPFRG0KMCqCwT5rG4kdIgJI7BCS8DUoO528EOWR36DsLW4oskOIiaZnp5ZFdmQuO2cwk3K0KrJYZCdQCguQPvX8RGuX8O9GY+j1sTRWKLFDkR0iEkjsEJLwdCl2nZBMFrvQwC9RPDsMiuwQYnJEnh2e7523pc5diVWSAJEdQOTbiVJFVqiGgoBI7NgcQY9nTUt4kR2WxvI3FwvwiB2r3SmskyCkQmKHkISvQdnkTmFpVYqgd4HxgMQOEYwcd2THYneiq5cXSebZCVVZFC8KojwyIlRDQcBTmcnz3tVbvoQb2WE3UoE8O3qtSuifRdEdIlxI7BCSEDw7blOisSsxUliAp/ScQWksQkyGRgmN0nWq622vHdZjR+7uyQxPr50opbEkRHbE3cnNQVJZYrEjKbITwrPDcZzgDySxQ4QLiR1CEr4GZRbZkbt7MkCRHSI4HMchh1Vk9bL8nJVQJ4JBGYhFGiv4EFAAUCo4oQdPIJOyxe5Ag8lTgdVskuDZsbIbqMDnFPLtEJFCYoeQhGdchOvkligNBQHvqewATTwnepIThV473TaH8PpE8+xEL43lNigHiewAoSuyatu6IbbzNJksIf1SrBrLt7pSjCB2otQNm0gdSOwQkvAdBJooDQUZ4lSWjoaAEj54RkZELnZY2blOo0yIiCYgrsaKbmQnlA+PpbIC9dph5uTyPB0AwOpwhozGsKhxIIMy4OnpRZEdIlxI7BCS0Pt4djxpLPkjO4B3KksXIOdPpC7RGAZa15Y4DQUZ+VEeGSHFswN4bn66AkR2mF9ncIFeiMY0hkhleSaeBxZalMYiIoXEDiEJX8+OJ42VGMJCHPomzw7hS7a7+25LL9IftQnUUJAhrsbqbVk9IK0aC/BMPjcHFDuuyE5ZTjoKM93T2SWLncA3UCR2iEghsUNIgokdm4OHxe4Q0liJ4NkBvBsLBsv5E6lJNCI7u062AwCGFuqjsqZowNJY3TanpCnkoWCRnUBDQBkZIUZG1LgjO6U5OhS4xU6jKfjICFaN5dskVAyJHSJSSOwQkhB7YswWR8LMxWKI01jpFNkhfBAMyr3w7FQfawUATBiYE5U1RYMMrUrwz0TDpGwR0ljB/4ZCTT4XIju56YLYCRXZCacay0hihwgTEjuEJFRKhXC3Z7bYhblYiWNQpjQWERgW2WmLUOyYLXbsqTMCSCyxA0S3144njRX80pAeohrrhCiyIzmNJUR2qPSciD4kdgjJ6IWREXZPZCdB0ljicnNqKkj4Inh2IhwGur2mDQ4nj5KsNJRkJ45nBxCXn/e+IsuTxgp+w5ARZD5Wt80hCJvSnHRRGkuqZ4fEDhF9SOwQkvH01vBEdhKlBFd8gqTIDuFLbz07QgqrPDdqa4oW0ey1IzQVDFGNlS6aj+ULi+pkalXISlejMNPVbTpYZMfmcApRpaBiR0dih4gMEjuEZMTDQD19dhIjsiMOfZNBmfBF7NmJpGrpByZ2BmRHc1lRIZ+lsUw9hVyn1Y5Xvz6MX76ySTBYB8MiYeo54Pkb81d6zvw6/XPSwXGcpMiOeOwEpbGIWEBXBUIy4mGgiZbGEhuoyaBM+MIiO1b3MNBwUp1OJ4+tx11i54wEjuycMnvERKfVjn9tOo6Xvz4kpLdW/XgSY/pnBX0vKeMiAM/fmL/ZWOJKLACSPDvsfKJVKaBWBr4HJ7FDRAqJHUIyGSLPTsIZlN1rUys5r9ERBAG4UpsapQJWhxMtZmtYYudAYwdM3XboNEqMLM6M4SojI19kUPYnctRKDjYHLymF122X1lRQFzSN5anEAiBEdtq7bOi2OfxGjZhfJ9T5hIkdi90Z8L0A4EizGXf8+0fcOmUIZo/tF/Q9idSArgqEZJigONVhhc3hSgUkTOm5++KVTqMiCD94DwMNLyrww7EWAMD4smyogkQd5CLPHdnZcrQV5z79FZ74eC+aO6wYkKvD0/+vEo/OHQMAOCVB7Fgkjotgf2/+DMonWrwjO1npamHqfCBfEYsQBUthAS4/j1Lh6l4dLLrzr03HsPNkO1ZsqQn6fkTqkBi35USfQO8+wdW1u05mCs47fSQn7CQZ6mRJpC45Og0ajJawe+0wc/IZCVZyzmBpLJYmGpCrw21Th+LS0/pDrVTgi70NAKTNBeuWOAg0PUifHRbZKc1xRXaYb+dkWxeaTBZBBIkxSajEYu9lSFOhtdOG9i4bigxpfrf79tApAKHL3YnUQdbblGXLlqGyshIGgwEGgwFVVVX45JNPAAAtLS24/fbbMWLECKSnp2PAgAG444470N7ubbLjOK7Hz4oVK+T4OEkPExJsIGJmmjphZgSx0nPy6xCBiLTXDhM7pyeo2BnVLxO5GRohkvPFb8/DlWeUCd6XHPfnPiWhNF2qZ0eYjRWkGqtMJGryQ5iUpUZ2gNC+nRazFXvdPZGaojQzjOj7yHobXFpaiqeeegrDhg0Dz/N46623MHfuXPz444/geR61tbV49tlnUVFRgWPHjmHBggWora3FypUrvd7njTfewKxZs4Tfs7Oz4/xJUgNmUGaRnUQpOweA08tyMLI4E3MoP08EQKjICqP8vMlkwbFTneC4xBU72ToNNv9+GlQKzu/NR16G9InvFqmRHbX3rDyG2WIX0mWluZ5+RKFMyqyhYGY4YifAnLON7qgO4BqQ6nDyQuqLSF1kvVpdfPHFXr8/8cQTWLZsGTZt2oQbb7wR77//vvDckCFD8MQTT+Caa66B3W6HSuVZenZ2NoqLi+O27lSFTROvd0d2EqUSC3D13/j0rnPlXgaRwHg8O9LFDovqjCjKTKjvuy/BKphYZKfT6ghq6gXC8OwEmHp+0j0ZPitd7XW8QpWfd4QR2TGEiOx8e6hZ+LeTd4lbtn8idUkYt53D4cCKFStgNptRVVXld5v29nYYDAYvoQMACxcuRH5+Ps466yy8/vrrUZn+S/SEnYhYlUeiVGIRhBRyI5iPVe02JydqVEcKmVoV1EpXZCNUVIt1UNaGGBfBqrF8p577+nUYISM7zLMj4ZwSKo313cFmr99DDSAlUgPZr1Y7d+5EVVUVuru7odfrsWrVKlRUVPTYrrm5GY899hhuvvlmr8cfffRRTJ06FTqdDp999hluvfVWdHR04I477gi4T4vFAovF80dnNBqj94GSGL3PNOJEvtMlCF9yhHSO9GqsHxLcnCwFjuOQo9Og0WRBi9kadNyFMBsrxCDQ9ABNBWuESizvfXiGgfoXHiwdFlYay4/YOdnWhaOnOqHggJLsdJxo7SKTMgEgAcTOiBEjsG3bNrS3t2PlypWYP38+NmzY4CV4jEYj5syZg4qKCjzyyCNer3/ooYeEf5922mkwm8145plngoqdJUuWYPHixVH/LMmOb2fiRCk7JwgpMM+O1DRWt80hdB0+Y2DiNRMMh9wMj9gJRrizsawOJ2wOp5BGE3rs+FRchRoZEU4aK5jYYVGdytJsGNLVJHYIAdnTWBqNBkOHDsWECROwZMkSjBs3Di+88ILwvMlkwqxZs5CZmYlVq1ZBrQ5+gZ04cSJOnDjhFbnxZdGiRWhvbxd+amqoF4MUfMtCKY1F9CVYZEeqQXnnyXbYHDzy9VqhQV5fJVeCSdnucMLudFkApM7GArzLz0NHdgKJHdd7hCo9Bzxix+hP7LjNyZOH5qFAL20AKZEaJNzVyul0CkLFaDRi5syZ0Gq1+Oijj5CW5r+ngpht27YhJycHWm1gQ5pWqw36POEf37suSmMRfQnm2ZFSlQQAPxz1pLASpcVCpEgReiyFBYSO7GiUCqgUHOxOHl1WhyBATrQxz453ZEcQOx0W8Dzf43h2uDuyhyN2fCM7PM/jO7c5+ewh+fjfAde/KbJDADKLnUWLFmH27NkYMGAATCYTli9fjvXr12Pt2rUwGo2YMWMGOjs78fbbb8NoNAremoKCAiiVSqxevRoNDQ2YNGkS0tLSsG7dOjz55JO499575fxYSYuv2KHIDtGXyHZPzG7ttPm94PoiNBMs77t+HUaeBLHTLeqZEyqyw3Ec0jVKmLrtMFs95edCj51cb7HDRlrYHDzaOm2C+GKYWWSnFwblQ01mNBgt0KgUmDAwB/vqTQCo1w7hQtarVWNjI+bNm4e6ujpkZWWhsrISa9euxQUXXID169dj8+bNAIChQ4d6ve7IkSMoLy+HWq3G0qVLcffdd4PneQwdOhTPPfccbrrpJjk+TtLje9dFnh2iLyEeBtppdQT1h/C8Z/hnX67EYkjpMdTtjuxolAooJPSl0bnFDjMpm7ptaHObv/v7pLG0KiWydWq0ddrQaLL0EDumKHh2WFTnjIE5SFMrJQ0gJVIHWcXOa6+9FvC5KVOmhCwhnzVrllczQSK2ZFA1FtGH0WmU0KgUsNpdw0CDXVgPN5vRYrZCo1JgTEnwSeF9gVwpaSyJZecMV8GCRaikYlGdHJ3abzqqMFOLtk4bmkwWjPAZqGqWOC4CcPXUAvyInYMuv87ZQ/IAhPYJEamF7AZlou/gOynaQGksog/BcZxk3w5LYY0rzYImREqnLyBF7HRLbCjISPeZfB4ohcXwNBbsWX7eEY7Y8RPZcTh5bDzsFjtD8732R2KHAEjsEGGgVHBeU8UpjUX0NcS+nWBUu83JE/p4yTlDSjUWGwIayq/DyPDptVPT4r+hICNY+XkkTQUtdqfgM9pTa0R7lw16rQqV/bPc+9MK791p7TmdnUgtSOwQYSEO/VMai+hrsIv+kaaOoNv94O6c3JebCYqRlsaKLLLjm8by7bHDCBRpsdqdsLr9QnpNaLGj16qEWVes/Jz5dSYOyoXK3fNHr1UhzZ2So+gOQWKHCAtxF2WqxiL6GhMHufwcz6zdj/3uah1fWs1WHGoyA0gOczIgjuzY4HT690KyyE6aRM8OGxnBJp/XBBgVwQjU90Y8TNTXF+gPjuOEFHqbW+x8e8g7hcW2o1QWwSCxQ4SFOLJDYofoa9x6/hBUDc6D2erAr/+xxW+kg1VhDS7IEERCX4dVYzmcPIzd/lN4gkE5xKgIBvPwsbJxFtnx7bHDKDT4Fx4shZWmVghRmVCIfTtWuxNbjrgicZOH5nltxwQWiR2CxA4RFkzsZGiUkk9MBJEoqJUK/PXq01GWm46ali7c+q9q2BxOr22SYR6WLxqVQpg7FSiVJczFCjeyY2VpLPeoiADdpj2RHW+DsqmbmZOlp8UFsdNpw4/HW9FlcyAvQ4Phhd5VXoJPiHrtpDx0tSLCglVLZJJfh+ij5GRo8Nr8M5GhUWLT4RY8unqP1/OsEmtCEokdQDwI1b/YEeZiSY3suFNOnVYH2jttgmjpnx1eZIc1JfQdNBwMgyiyw0ZEVA3J69EfSKgAM5LYSXVI7BBhwSI7hnRKYRF9l+FFmXjhF6eB44B/bjqGtzcdA+Ayy26vaQOQPJVYDJaSO9URSOy4IjtS++zo1O40ltUh+HXy9RqvuVliCvSuKIux2+7VrbmjW3olFiPLS+y4zMmTRX4dYZ/k2SHckNghwoJNO6bIDtHXmV5RhHtnjAAAPPLRbmw8dAq7a9thsTuRrVNjSEGGzCuMLqHKz8ON7DAzcZfVHtKvA7hukFjPIrH4ECaeS6jEYjCxU2/sxo/H2wB4mgmKEc/kIlIbEjtEWAiRHTInE0nArVOG4OfjSmB38rj1X9X4cFstAGDCgL4//NMXIbITwrOjDbf03OoQ/DqBKrEAd3WUvqf4YGInnIIHJnY+39MAu5NH/+x0DPDTzJBGRhAMEjtEWHjSWBTZIfo+HMfh6f9XibH9s9DaacOb3x0FAExIguGfvgiRnQBip9sWXlNBj0HZEbJ7MoP5dsQeGnMYc7EYTOwcbna1CJg8NM+vOA3WtZlILUjsEGFxxsAcaJQKoV8JQfR10tRKvDrvDOHCCLgiO8mGZxio/9LzcMdFsNLzTqs9ZPdkhr/IjqcaK3yxwzh7SE+/DuARO80d1oD9hYjUgMQOERbnDi/ArsUz8auJA+ReCkFEjeKsNLxy7QRoVApk69QYV5Yt95KiTp7QRdl/SscSYVPBTlFkJ5hnBxB5aIyeSEs4Q0AZPcWO/5uvfLe4cjj5kPPQiOSGjBdE2CTDYESC8OW0ATlYd/e54MBJjm70JVjpeUuAuWBCNVa4TQWtdrS4K7zKQkR2/PW9CWcIKEMsdoYV6lFoSPO7nVqpQG6GBi1mKxpNFuTptX63I5IfumoRBEG4GZiXgQF5waMTfZXcEJGdSMdFNLRbYHYPAy3JDpHG8tP3JpwhoIwsnUfsBIrqCPukLsoESOwQBEGkBB6DcqBxEeF6dlzbWd0dqAsztSFfW+inFLyjFwZlwHselj+o1w4BkNghCIJICXLdBuUOi13w54hhj0mvxvIWJ6EqsQD/woN5djLDEDt5GVpolApolApMClEs4U9gEakHeXYIgiBSAEO6CkoF5zLrmm0ozvKOwghNBcOM7DBCVWIB3iMjnE4eCgUnVGOFE9lJ1yjx6vwzoFJwXiktf9DICAKgyA5BEERKwHGcqPy8Z2VSuINA09Xhi528DJfwsDt5tHW50mnCbKwwG5WeN7zA74gIX6iLMgGQ2CEIgkgZPOXnPcWOp6mgtMiOQsF5CZ6yEGXngKuSM8cdiWGN/joi6LMTDp7UGTUWTGVI7BAEQaQIORkuodHip+eMp6mg9MuCOJUVqscOQyg/d/t2zBaXyIq92KHITipDYocgCCJFCDYywmNQlt5jSKcVRXZyQ6exAG8PjcXuEKq5wvHshEOhMDKCxE4qQ2KHIAgiRQg2DDTccREAoFO7BArHAf2ypIkdcXUUi+oAMYzs6F2RJFO3XUjVEakHiR2CIIgUgZWf+4vshDsIFPBEdooNaZI7q4vTSsyvk65WQqmIzZR5Q7pKWBulslIXEjsEQRApQm4AgzLP86JqrDAiO27PjhRzMqNAlFYyWVwVWeFWYoUDx3FCF2VKZaUuJHYIgiBShJwAYocJHSA8g3K6O40lpeycIa6OirU5uec+SeykKiR2CIIgUgTBoOxTjcVGRQDhGZQz3GmscMQOq8ZqNFnQwSI78RI71GsnZaEOygRBEClCIIMyq8RScIBaKd07c9nppahp6cTF40okv8bLs+OO7GRoYztlvpAiOykPiR2CIIgUQVx6zvM8OM4lbMSVWOwxKZw3vADnDS8Iaw1M7Ji67Wh2iw+9NvjIh95CjQUJSmMRBEGkCGxchN3Jw+iuhAKA7jCHgPYGQ5pK2M/RU2YAgD7GkR3y7BAkdgiCIFKENLUSGe4KKnH5uSWCHjuRwnGcID6ONLvFTgyrsYCeXZuJ1IPEDkEQRAohVGSJTMosshMPsQN4PDSHm1xiJ1bdkxkU2SFI7BAEQaQQwjDQDpHYiaChYG9g4qO2vQsAkBnHaiye52O6LyIxIbFDEASRQviL7LA0ljZukR1XWonpjlhHdvL1rs9sc/Bo67TFdF9EYkJihyAIIoXw10VZSGPFObLDiHWfHa1KiWydq+KLeu2kJiR2CIIgUgh/87G64xzZ8RU7mTE2KAPwjIwwkthJRWQVO8uWLUNlZSUMBgMMBgOqqqrwySefCM93d3dj4cKFyMvLg16vx+WXX46Ghgav9zh+/DjmzJkDnU6HwsJC3HfffbDb7b67IgiCIOB/ZIQlzpGdQh+xE+s0FiD27VCvnVREVrFTWlqKp556CtXV1fjhhx8wdepUzJ07F7t37wYA3H333Vi9ejXee+89bNiwAbW1tbjsssuE1zscDsyZMwdWqxXfffcd3nrrLbz55pv44x//KNdHIgiCSGjy/KWx4lh6DsQ/jQVQF+VUR9YOyhdffLHX70888QSWLVuGTZs2obS0FK+99hqWL1+OqVOnAgDeeOMNjBo1Cps2bcKkSZPw2WefYc+ePfj8889RVFSE8ePH47HHHsMDDzyARx55BBqNRo6PRRAEkbD4LT2PczUWMygz4iF2qPw8tUkYz47D4cCKFStgNptRVVWF6upq2Gw2TJ8+Xdhm5MiRGDBgADZu3AgA2LhxI8aOHYuioiJhm5kzZ8JoNArRIX9YLBYYjUavH4IgiFTAX2SHTT2PV2QnT6+BeCpFrJsKAh6x00hiJyWRXezs3LkTer0eWq0WCxYswKpVq1BRUYH6+npoNBpkZ2d7bV9UVIT6+noAQH19vZfQYc+z5wKxZMkSZGVlCT9lZWXR/VAEQRAJil/Pjo01FYzPJUGtVAhGaSDOnh0SOymJ7GJnxIgR2LZtGzZv3oxbbrkF8+fPx549e2K6z0WLFqG9vV34qampien+CIIgEgUmMkzddtgcroiOJ40Vn8gO4O3bydDEw7NDIyNSGdmnnms0GgwdOhQAMGHCBGzZsgUvvPACrrrqKlitVrS1tXlFdxoaGlBcXAwAKC4uxvfff+/1fqxai23jD61WC61WG/B5giCIZCUrXQ0FBzh5V/l5oSFNlMaK3/1vQaYW++pN0GmUUCqkT1rvzf4A6rOTqsge2fHF6XTCYrFgwoQJUKvV+OKLL4Tn9u/fj+PHj6OqqgoAUFVVhZ07d6KxsVHYZt26dTAYDKioqIj72gmCIBIdhYITpp8zk3K3Lb6zsQCP+IiHORnw9Nlp67QJpfZE6iBrZGfRokWYPXs2BgwYAJPJhOXLl2P9+vVYu3YtsrKycOONN+Kee+5Bbm4uDAYDbr/9dlRVVWHSpEkAgBkzZqCiogLXXnstnn76adTX1+PBBx/EwoULKXJDEAQRgNwMDU6ZrcJ8LKGpYJyqsYD4i51snRpqJQebg0dzhxX9s9Pjsl8iMZBV7DQ2NmLevHmoq6tDVlYWKisrsXbtWlxwwQUAgP/7v/+DQqHA5ZdfDovFgpkzZ+Kvf/2r8HqlUok1a9bglltuQVVVFTIyMjB//nw8+uijcn0kgiCIhMe3/JxFOuLVQRnweGjiUYkFABzHoUCvRW17N5pMFhI7KYasYue1114L+nxaWhqWLl2KpUuXBtxm4MCB+Pjjj6O9NIIgiKTFd2REvJsKAsDAXB2Anj13YklBpkvsNBqpi3KqIbtBmSAIgogvue4p4KeY2LHHt6kgAJw/shDPXjEOEwflxm2fZFJOXUjsEARBpBi+kR2LDJEdpYLD/5tQGrf9AUABlZ+nLAlXjUUQBEHEltwM/5GdeA0ClQtqLJi6JPc3myAIgugBEzutnd6RnXgalOWARkakLiR2CIIgUgzPyAgbAE81VjybCsoB67UjV2Snrr0L5z+7Hr97f4cs+09lkvubTRAEQfTAMwzUddEXqrHiOC5CDgoN8oqdR1fvwZFmM97fegJWd9dqIj6Q2CEIgkgxWGSn1WwDz/Oe2VipEtnpsIDn+bju+6v9jfhkl2tAtc3B40CjKa77T3WS+5tNEARB9IBVY1kdThi77LA7XRf+ZI/sMM+O1e763PGi2+bAwx/u9npsT60xbvsnSOwQBEGkHOkaJdLdZuTa9i7h8XiWnstBmloJg7tjc1NH/BoLLlt/CMdbOlFsSMMvziwDAOwmsRNXSOwQBEGkIKwiq04kduLZVFAu4l2RdaTZjGUbDgEAHrqoAmeWu5ooUmQnviT/N5sgCILoARM7tW2uCIdGqYBCwcm5pLgQz147PM/j4Y92w2p34pxh+bhwbDEqSgwAgD11Rjid8fUNpTIkdgiCIFKQHJ/ITrKbkxnx7KL8ya56fP1TEzRKBR6dOwYcx2FooR4alQIdFjtqWjtjvgbCRWp8uwmCIAgvWPl5nTuyo01yczKjME6RnQ6LHY+u3gMAWDBlCAblZwAA1EoFRhRlAiDfTjwhsUMQBJGC5LgrsphBOdkbCjLilcZ64fOfUG/sxoBcHW6dMsTrudHuVNbu2vaYroHwkBrfboIgCMKL3Aw1AKCu3RXZSfZKLAbrtRNLg/L+ehNe//YoAGDx3NE9jq3g26HITtwgsUMQBJGC5Ga4LvpM7KRCJRYQ+8gOz/N48D874XDymDW6GOePKOyxjSeyQ2InXqTGt5sgCILwgkV22NiCVInsCCMjOmIjdt7fehJbjrZCp1HijxdX+N1mZLEBHOeKLiXDBPaXNxzCzP/7GsdOmeVeSkBI7BAEQaQgLLLDSBnPjjuN1WK29phPxfM8dp1sx8sbDuF/B5rCfm+e5/HiFwcAAHdMG4aS7HS/22VoVYJhua/7dv7+v8NY8sk+7G8wYfX2WrmXExCV3AsgCIIg4g+L7DBSpRorR6eBSsHB7uRxymxBZpoa3xxowpf7GrF+f5Pg5UlTK7D1oQug00i/TB5qMuN4Syc0SgXmVQ0Mum1FPwMON5mxp86IKX5SXX2Bd7fU4PH/7hV+334icYUbiR2CIIgUhFVjMVIlsqNQcMjXa1Fv7MZN//gB++tNsDk8zf10GiU4AGarA98caMaM0cWS33v9/kYAwMTBuSFF0uiSLKzZUddnfTuf7qrD7z7YAQA4Z1g+/negGdtr2uRdVBBS49tNEARBeJGt04ATNUxO9iGgYorcvp1dJ42wOXgMys/ADZMH4Z83noUf/3gBrjjDNb/qy32NYb3v+v2u1Jc/U7Ivo/twRdY3B5pxx7+3wckDV55RipevnQClgkOjyYL69vjNHAsHiuwQBEGkIEoFh+x0NVo7bQBSp4MyANx1wXCsrD6B0wfkYOrIQsE/w5g2qhBvfncUX+xrhNPJSxqjYbbYsfnIKQDA+SNDix1Wfn6k2YwOix16bd+4HG893oqb//kDrA4nZo8pxpLLKqFUcBhWqMe+ehO2n2hDcZb0aFi8SJ1vN0EQBOEFm48FpI5nB3BFXpb+6nTc+LNBPYQOAJw1KBcZGiWaTBbskmgg/vZgM2wOHgPzdH7f05d8vVaIMO2rkxbdWb75OB5fswcOmWZq7as34vo3tqDT6sA5w/Lx/C/GQ+kWguNKswEAO060ybK2UJDYIQiCSFHEYidVSs+loFUpce7wAgDA53ulpbK+CiOFxRhdkgVAWr+dVrMVf/xwF/7+zRF8e7BZ8j6ixbFTZlz72vdo77Lh9AHZePnaCV4CubLM9Vl2JKhJmcQOQRBEiiI2KadKU0GpTHWnor7c1xByW57nBXPylBEFkvcRztiIj3fVwe6O6Hy1PzwvUW851WHBNa9tRpPJgpHFmXjjurN6GLBZZGd7TRt4PvGmudO3myAIIkXJ01NkJxDnjywEx7lMzKFMt/sbTKhr70aaWoFJg/Mk70MwKUtIY320zdPD5qswjdO95alP9qGmpQsDcnX4xw1nIUun7rHNiOJMaFQKGLvtOHoq8aa5k9ghCIJIUcSRnVQpPZdKvl6L8WXZAEJXZX21z5XCOntIfliisaKfK/XzU30HbA5nwO3q2rvw/dEWAIBKweHoqU4caY5Pt+JtNW14r/oEAOD5X4xHoSHN73ZqpUIQb4no26FvN0EQRIqSqgZlqUwfVQQA+GJv8FQWSyudH0YKCwDKctORmaaC1eHEgYaOgNut2V4HngfOKs/FWYNyAYRfFh8JTiePRz7aDQC47PT+OH1ATtDtPamsxPPtkNghCIJIUbwNynQ58IX5dr452Iwuq8PvNu1dNlQfawWAsDshcxyHin6hfTsfbj8JAPj5+BJhTevj4Nv54MeT2FbThgyNEr+bNTLk9pWlzKTcFuOVhQ99uwmCIFKUHKrGCsrI4kz0z06Hxe7Ed4f8V0B9c6AZDiePoYV6lOXqwt4Hq8gK5Ns51NSBXSeNUCk4XDi2nyCoNh9ugdliD3t/UjF12/CnT/cBAG6fNixg+kpMpTuys6u2HfYgaTk5ILFDEASRouRlUDVWMDiOEyIpXwRIGwlVWMPDS2ExKoSKLP9ihxmTzxmWj9wMDYYUZGBArg5WhzOmJeh/+fIgmkwWDMrPwPWTyyW9ZnB+BjK1KnTbnPgpSFpODujbTRAEkaJ4G5QpsuOPaaPcJeh7G3uUVDudPNb/5O6vI6Frsj+YqXdvrRFOn2aBPM8Lk8R/Pr4EgEuAMW9QrErQDzd14PVvjwAA/nhRhWQ/l0LBYWyCprJI7BAEQaQo3qXndDnwx6TBedBplKg3dveIvuypM6LJZEGGRokzyoObdwMxtFAPjUoBk8WOmlbvku1dJ4043GxGmlqBCyo8IxiYsPpqX1NMeto8tmYPbA4e548oCFvEsVRWok1Ap283QRBEipKuVgrpK6rG8k+aWomfDc0HAHzh002Z9buZPDQ/4uOnViowoigTQM+hoB+5jcnTRhV5zc6aNDgPaWoF6o3d2Ftnimi/gfhyXwO+2t8EtZLDQxdVhP36cRTZIQiCIBIJjuNw+oAcZGpVEZlrUwUhleXTTVkoOY8whcXwVGR5xI7DyeMjlsIaV+K1fZpaiclD8r3WEA0sdgceW7MXAHDDzwZhcIE+7PeodPcm2l9vQrfNfwWbHJDYIQiCSGH+eeNZ+G7RVGSl9+yKS7hgYmb7iXY0Gl3dlFvMVvxY0wYgvBER/hjdv2f5+fdHWtBgtCAzTeX3/acIqazoiZ03vj2KI81mFGRqcfvUYRG9R0lWGvL1GtidvKTO0PFCVrGzZMkSnHnmmcjMzERhYSEuueQS7N+/X3j+6NGj4DjO7897770nbOfv+RUrVsjxkQiCIPoUKqUCmWkkdIJRmJkmpGdYJOV/B5rA867y9H5Z6b16/9F+KrJYVGf2mGK/KTJmUt56vBVtndZe7R8AGo3deOmLAwCA380a6ZU2CweO4zy+HbcYTARkFTsbNmzAwoULsWnTJqxbtw42mw0zZsyA2exqg11WVoa6ujqvn8WLF0Ov12P27Nle7/XGG294bXfJJZfI8IkIgiCIZGSau5sym4LOIirhNhL0x8hiAzgOaDRZ0GSywGp34uOddQCAueP7+31NaY4Ow4v0cPLABndFWG946pN9MFsdOG1ANi49zf8+pcI6KSfSBPTIpFuU+PTTT71+f/PNN1FYWIjq6mqce+65UCqVKC4u9tpm1apVuPLKK6HXe+cSs7Oze2xLEARBENFg6shCPLfuJ3xzoBmdVrsgMMIdEeGPDK0Kg/IycLjZjD11RtgdTrR32VCQqQ06WPT8kYX4qaED6/c3BRRFUni/+gQ++NFlhn7k4tFQKLiI3wsAKstcUbDtCWRSTijPTnu7SwXm5ub6fb66uhrbtm3DjTfe2OO5hQsXIj8/H2eddRZef/31oOV4FosFRqPR64cgCIIgAjG6xIBiQxq6bA78bcNhtHbakJmmwukDIys598XTXLAdH7obCV5U2Q/KIMLj/BGe0REOZ2Ql6DtOtGHRqp0AgDumDcM4t8G4N7DIzuEmM4zdtl6/XzRIGLHjdDpx1113YfLkyRgzZozfbV577TWMGjUKZ599ttfjjz76KN59912sW7cOl19+OW699Va89NJLAfe1ZMkSZGVlCT9lZWVR/SwEQRBEcsFxHKa6q7Je3nAIAHDusAKoldG5jLKxEdVHW7Fuj6vqy7cKy5cJA3OQmaZCa6ctoihKc4cFv/lnNax2J6aPKsRd0yIzJfuSm6FBaY7Lx7QrQVJZCSN2Fi5ciF27dgU0Fnd1dWH58uV+ozoPPfQQJk+ejNNOOw0PPPAA7r//fjzzzDMB97Vo0SK0t7cLPzU1NVH7HARBEERyMt0tdix219yn3lZhiWEm5S/2NaLL5sCAXB3Gh4iyqJUKnOseUxFuVZbN4cSt/9qKuvZuDC7IwHNXje91+krMuARrLpgQYue2227DmjVr8NVXX6G0tNTvNitXrkRnZyfmzZsX8v0mTpyIEydOwGKx+H1eq9XCYDB4/RAEQRBEMM4eku/Vafq8KIodlsZi/HxcCTgutPhgqaxw++08vmYPvj/SAr1WhVeuPQOGKFfksQnoiVKRJavY4Xket912G1atWoUvv/wSgwYNCrjta6+9hp///OcoKAj95dq2bRtycnKg1WqjuVyCIAgihRF3Ux7bPwuFmaEngUslX69FkcFzzZo7PngKi8GiS7tOGoUeQKF474cavLXxGADg/64aj6GF4TcPDAXz/iRKJ2VZq7EWLlyI5cuX48MPP0RmZibq6+sBAFlZWUhP9/QtOHjwIL7++mt8/PHHPd5j9erVaGhowKRJk5CWloZ169bhySefxL333hu3z0EQBEGkBtdMGogv9zXimkkDov7eo0uy0GBsxMjiTAxzj5AIRb5ei3GlWdh+oh3r9zfhyjODe1C317ThD//ZBQC4a/owXFBR1Ot1+2NM/yxwHFDb3o0mkwUFmfIGH2SN7Cxbtgzt7e2YMmUK+vXrJ/y88847Xtu9/vrrKC0txYwZM3q8h1qtxtKlS1FVVYXx48fj5ZdfxnPPPYeHH344Xh+DIAiCSBGmjCjE4SVzcNWZ0Rc70929fK47uzys17EOz1+G8O00mTyG5AsqinBHhF2SpaDXqjDUPW4iEaI7HB+Lkal9DKPRiKysLLS3t5N/hyAIgpAFnufR3GENOwqyvaYNc5d+C71Wha0PXQCNqmccw2p34uq/b8KWo60YUpCB/yycHPPO2b99dzve33oCd0wbhnsuGB6TfUi9fsuaxiIIgiAIwgXHcRGle8b2z0K+XoPmDit+ONqCs4fmo9HUjW3H27D9RBu217Rj+4k2mLrtyNSq8Mq8M+IyImRcWRbe33oiISI7JHYIgiAIog+jUHA4b3gh3t96Ag/+Zxe6bQ7Utvc0KxvSVHjhl6dhSATTzCNBPCOL53lJ1WWxgsQOQRAEQfRxpo1yiZ3Dza7ZkhwHDC/MxPiybIwry8a4siyMKMqEKkpNEKUwql8m1EoOrZ02nGjtQlmuLm779oXEDkEQBEH0cWaOLsbvZo8EAIwvy8aY/lkRTy6PFlqVEqP6GbDjhCuNRmKHIAiCIIiIUSo4LDhviNzL6EFlaRZ2nGjHjhPtuKhSWu+gWJAQHZQJgiAIgkg+xL4dOSGxQxAEQRBETGAzsnadbI94Mns0ILFDEARBEERMGFqoh06jRLZOg0aTtHEWsYA8OwRBEARBxASlgsPGRdOQlR77vj7BoMgOQRAEQRAxQ26hA5DYIQiCIAgiySGxQxAEQRBEUkNihyAIgiCIpIbEDkEQBEEQSQ2JHYIgCIIgkhoSOwRBEARBJDUkdgiCIAiCSGpI7BAEQRAEkdSQ2CEIgiAIIqkhsUMQBEEQRFJDYocgCIIgiKSGxA5BEARBEEkNiR2CIAiCIJIaldwLSAR4ngcAGI1GmVdCEARBEIRU2HWbXccDQWIHgMlkAgCUlZXJvBKCIAiCIMLFZDIhKysr4PMcH0oOpQBOpxO1tbXIzMwEx3FRe1+j0YiysjLU1NTAYDBE7X2TFTpe4UPHLDzoeIUPHbPwoOMVPr05ZjzPw2QyoaSkBApFYGcORXYAKBQKlJaWxuz9DQYDfenDgI5X+NAxCw86XuFDxyw86HiFT6THLFhEh0EGZYIgCIIgkhoSOwRBEARBJDUkdmKIVqvFww8/DK1WK/dS+gR0vMKHjll40PEKHzpm4UHHK3zicczIoEwQBEEQRFJDkR2CIAiCIJIaEjsEQRAEQSQ1JHYIgiAIgkhqSOwQBEEQBJHUkNiJIUuXLkV5eTnS0tIwceJEfP/993IvKSH4+uuvcfHFF6OkpAQcx+E///mP1/M8z+OPf/wj+vXrh/T0dEyfPh0HDhyQZ7EJwJIlS3DmmWciMzMThYWFuOSSS7B//36vbbq7u7Fw4ULk5eVBr9fj8ssvR0NDg0wrlp9ly5ahsrJSaFJWVVWFTz75RHiejldwnnrqKXAch7vuukt4jI6ZN4888gg4jvP6GTlypPA8Ha+enDx5Etdccw3y8vKQnp6OsWPH4ocffhCej+W5n8ROjHjnnXdwzz334OGHH8bWrVsxbtw4zJw5E42NjXIvTXbMZjPGjRuHpUuX+n3+6aefxosvvoi//e1v2Lx5MzIyMjBz5kx0d3fHeaWJwYYNG7Bw4UJs2rQJ69atg81mw4wZM2A2m4Vt7r77bqxevRrvvfceNmzYgNraWlx22WUyrlpeSktL8dRTT6G6uho//PADpk6dirlz52L37t0A6HgFY8uWLXj55ZdRWVnp9Tgds56MHj0adXV1ws8333wjPEfHy5vW1lZMnjwZarUan3zyCfbs2YM///nPyMnJEbaJ6bmfJ2LCWWedxS9cuFD43eFw8CUlJfySJUtkXFXiAYBftWqV8LvT6eSLi4v5Z555Rnisra2N12q1/L///W8ZVph4NDY28gD4DRs28DzvOj5qtZp/7733hG327t3LA+A3btwo1zITjpycHP7vf/87Ha8gmEwmftiwYfy6dev48847j7/zzjt5nqfvmD8efvhhfty4cX6fo+PVkwceeID/2c9+FvD5WJ/7KbITA6xWK6qrqzF9+nThMYVCgenTp2Pjxo0yrizxOXLkCOrr672OXVZWFiZOnEjHzk17ezsAIDc3FwBQXV0Nm83mdcxGjhyJAQMG0DED4HA4sGLFCpjNZlRVVdHxCsLChQsxZ84cr2MD0HcsEAcOHEBJSQkGDx6Mq6++GsePHwdAx8sfH330Ec444wxcccUVKCwsxGmnnYZXX31VeD7W534SOzGgubkZDocDRUVFXo8XFRWhvr5eplX1DdjxoWPnH6fTibvuuguTJ0/GmDFjALiOmUajQXZ2tte2qX7Mdu7cCb1eD61WiwULFmDVqlWoqKig4xWAFStWYOvWrViyZEmP5+iY9WTixIl488038emnn2LZsmU4cuQIzjnnHJhMJjpefjh8+DCWLVuGYcOGYe3atbjllltwxx134K233gIQ+3M/TT0niD7EwoULsWvXLi9vAOGfESNGYNu2bWhvb8fKlSsxf/58bNiwQe5lJSQ1NTW48847sW7dOqSlpcm9nD7B7NmzhX9XVlZi4sSJGDhwIN59912kp6fLuLLExOl04owzzsCTTz4JADjttNOwa9cu/O1vf8P8+fNjvn+K7MSA/Px8KJXKHs77hoYGFBcXy7SqvgE7PnTsenLbbbdhzZo1+Oqrr1BaWio8XlxcDKvVira2Nq/tU/2YaTQaDB06FBMmTMCSJUswbtw4vPDCC3S8/FBdXY3GxkacfvrpUKlUUKlU2LBhA1588UWoVCoUFRXRMQtBdnY2hg8fjoMHD9J3zA/9+vVDRUWF12OjRo0SUn+xPveT2IkBGo0GEyZMwBdffCE85nQ68cUXX6CqqkrGlSU+gwYNQnFxsdexMxqN2Lx5c8oeO57ncdttt2HVqlX48ssvMWjQIK/nJ0yYALVa7XXM9u/fj+PHj6fsMfOH0+mExWKh4+WHadOmYefOndi2bZvwc8YZZ+Dqq68W/k3HLDgdHR04dOgQ+vXrR98xP0yePLlHy4yffvoJAwcOBBCHc3+vLc6EX1asWMFrtVr+zTff5Pfs2cPffPPNfHZ2Nl9fXy/30mTHZDLxP/74I//jjz/yAPjnnnuO//HHH/ljx47xPM/zTz31FJ+dnc1/+OGH/I4dO/i5c+fygwYN4ru6umReuTzccsstfFZWFr9+/Xq+rq5O+Ons7BS2WbBgAT9gwAD+yy+/5H/44Qe+qqqKr6qqknHV8vK73/2O37BhA3/kyBF+x44d/O9+9zue4zj+s88+43mejpcUxNVYPE/HzJff/va3/Pr16/kjR47w3377LT99+nQ+Pz+fb2xs5Hmejpcv33//Pa9SqfgnnniCP3DgAP+vf/2L1+l0/Ntvvy1sE8tzP4mdGPLSSy/xAwYM4DUaDX/WWWfxmzZtkntJCcFXX33FA+jxM3/+fJ7nXSWIDz30EF9UVMRrtVp+2rRp/P79++VdtIz4O1YA+DfeeEPYpquri7/11lv5nJwcXqfT8ZdeeilfV1cn36Jl5oYbbuAHDhzIazQavqCggJ82bZogdHiejpcUfMUOHTNvrrrqKr5fv368RqPh+/fvz1911VX8wYMHhefpePVk9erV/JgxY3itVsuPHDmSf+WVV7yej+W5n+N5nu99fIggCIIgCCIxIc8OQRAEQRBJDYkdgiAIgiCSGhI7BEEQBEEkNSR2CIIgCIJIakjsEARBEASR1JDYIQiCIAgiqSGxQxAEQRBEUkNihyCIlKS8vBzPP/+83MsgCCIOkNghCCLmXHfddbjkkksAAFOmTMFdd90Vt32/+eabyM7O7vH4li1bcPPNN8dtHQRByIdK7gUQBEFEgtVqhUajifj1BQUFUVwNQRCJDEV2CIKIG9dddx02bNiAF154ARzHgeM4HD16FACwa9cuzJ49G3q9HkVFRbj22mvR3NwsvHbKlCm47bbbcNdddyE/Px8zZ84EADz33HMYO3YsMjIyUFZWhltvvRUdHR0AgPXr1+P6669He3u7sL9HHnkEQM801vHjxzF37lzo9XoYDAZceeWVaGhoEJ5/5JFHMH78ePzzn/9EeXk5srKy8Itf/AImk0nYZuXKlRg7dizS09ORl5eH6dOnw2w2x+hoEgQhFRI7BEHEjRdeeAFVVVW46aabUFdXh7q6OpSVlaGtrQ1Tp07Faaedhh9++AGffvopGhoacOWVV3q9/q233oJGo8G3336Lv/3tbwAAhUKBF198Ebt378Zbb72FL7/8Evfffz8A4Oyzz8bzzz8Pg8Eg7O/ee+/tsS6n04m5c+eipaUFGzZswLp163D48GFcddVVXtsdOnQI//nPf7BmzRqsWbMGGzZswFNPPQUAqKurwy9/+UvccMMN2Lt3L9avX4/LLrsMNH6QIOSH0lgEQcSNrKwsaDQa6HQ6FBcXC4//5S9/wWmnnYYnn3xSeOz1119HWVkZfvrpJwwfPhwAMGzYMDz99NNe7yn2/5SXl+Pxxx/HggUL8Ne//hUajQZZWVngOM5rf7588cUX2LlzJ44cOYKysjIAwD/+8Q+MHj0aW7ZswZlnngnAJYrefPNNZGZmAgCuvfZafPHFF3jiiSdQV1cHu92Oyy67DAMHDgQAjB07thdHiyCIaEGRHYIgZGf79u346quvoNfrhZ+RI0cCcEVTGBMmTOjx2s8//xzTpk1D//79kZmZiWuvvRanTp1CZ2en5P3v3bsXZWVlgtABgIqKCmRnZ2Pv3r3CY+Xl5YLQAYB+/fqhsbERADBu3DhMmzYNY8eOxRVXXIFXX30Vra2t0g8CQRAxg8QOQRCy09HRgYsvvhjbtm3z+jlw4ADOPfdcYbuMjAyv1x09ehQXXXQRKisr8f7776O6uhpLly4F4DIwRxu1Wu31O8dxcDqdAAClUol169bhk08+QUVFBV566SWMGDECR44cifo6CIIIDxI7BEHEFY1GA4fD4fXY6aefjt27d6O8vBxDhw71+vEVOGKqq6vhdDrx5z//GZMmTcLw4cNRW1sbcn++jBo1CjU1NaipqREe27NnD9ra2lBRUSH5s3Ech8mTJ2Px4sX48ccfodFosGrVKsmvJwgiNpDYIQgirpSXl2Pz5s04evQompub4XQ6sXDhQrS0tOCXv/wltmzZgkOHDmHt2rW4/vrrgwqVoUOHwmaz4aWXXsLhw4fxz3/+UzAui/fX0dGBL774As3NzX7TW9OnT8fYsWNx9dVXY+vWrfj+++8xb948nHfeeTjjjDMkfa7NmzfjySefxA8//IDjx4/jgw8+QFNTE0aNGhXeASIIIuqQ2CEIIq7ce++9UCqVqKioQEFBAY4fP46SkhJ8++23cDgcmDFjBsaOHYu77roL2dnZUCgCn6bGjRuH5557Dn/6058wZswY/Otf/8KSJUu8tjn77LOxYMECXHXVVSgoKOhhcAZcEZkPP/wQOTk5OPfcczF9+nQMHjwY77zzjuTPZTAY8PXXX+PCCy/E8OHD8eCDD+LPf/4zZs+eLf3gEAQREzie6iIJgiAIgkhiKLJDEARBEERSQ2KHIAiCIIikhsQOQRAEQRBJDYkdgiAIgiCSGhI7BEEQBEEkNSR2CIIgCIJIakjsEARBEASR1JDYIQiCIAgiqSGxQxAEQRBEUkNihyAIgiCIpIbEDkEQBEEQSQ2JHYIgCIIgkpr/D9T+eJvXM3L4AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.convergence_graph" + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(combi.cost_trace)\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Cost\")\n", + "plt.title(\"Cost convergence\")" + ] + }, + { + "cell_type": "markdown", + "id": "23969de4-f4b9-4e55-a09d-df4cbe4eb3ac", + "metadata": { + "tags": [] + }, + "source": [ + "# Optimization Results" ] }, { "cell_type": "markdown", - "id": "a5a26d5c-ffc0-40bc-9964-e9fb6e16f232", + "id": "9159388c-fe90-436d-b0aa-9cf6d6148c5f", "metadata": {}, "source": [ - "And print the optimization results:" + "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the Pyomo to qmod translator." + ] + }, + { + "cell_type": "markdown", + "id": "93232ede-dfc9-4eba-8270-e6039af36c38", + "metadata": {}, + "source": [ + "In order to get samples with the optimized parameters, we call the `sample` method:" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "9e5789ba-f1a5-4108-a0fa-a1b90870da1f", + "execution_count": 10, + "id": "f0fb79e2-719a-42f6-a9bd-18a67d490aad", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:18.670930Z", - "iopub.status.busy": "2024-05-07T15:06:18.670670Z", - "iopub.status.idle": "2024-05-07T15:06:19.843274Z", - "shell.execute_reply": "2024-05-07T15:06:19.842574Z" - }, - "tags": [] + "scrolled": true }, "outputs": [ { @@ -451,144 +363,102 @@ " \n", " \n", " \n", + " solution\n", " probability\n", " cost\n", - " solution\n", - " count\n", " \n", " \n", " \n", " \n", - " 98\n", - " 0.003\n", - " -4.8\n", - " [1, 2, 1]\n", - " 3\n", + " 1022\n", + " {'w': [1, 2, 0], 'budget_rule_slack_var': [1, ...\n", + " 0.000488\n", + " -4.5\n", " \n", " \n", - " 141\n", - " 0.003\n", - " -4.8\n", - " [1, 2, 1]\n", - " 3\n", + " 847\n", + " {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ...\n", + " 0.000488\n", + " -4.4\n", " \n", " \n", - " 35\n", - " 0.006\n", - " -4.8\n", - " [2, 1, 1]\n", - " 6\n", + " 367\n", + " {'w': [0, 3, 0], 'budget_rule_slack_var': [1, ...\n", + " 0.000977\n", + " -3.9\n", " \n", " \n", - " 5\n", - " 0.010\n", - " -4.8\n", - " [2, 1, 1]\n", - " 10\n", + " 101\n", + " {'w': [1, 3, 1], 'budget_rule_slack_var': [1, ...\n", + " 0.001465\n", + " -3.7\n", " \n", " \n", - " 210\n", - " 0.002\n", - " -4.8\n", - " [1, 2, 1]\n", - " 2\n", + " 1023\n", + " {'w': [2, 1, 0], 'budget_rule_slack_var': [0, ...\n", + " 0.000488\n", + " -3.5\n", " \n", " \n", "\n", "" ], "text/plain": [ - " probability cost solution count\n", - "98 0.003 -4.8 [1, 2, 1] 3\n", - "141 0.003 -4.8 [1, 2, 1] 3\n", - "35 0.006 -4.8 [2, 1, 1] 6\n", - "5 0.010 -4.8 [2, 1, 1] 10\n", - "210 0.002 -4.8 [1, 2, 1] 2" + " solution probability cost\n", + "1022 {'w': [1, 2, 0], 'budget_rule_slack_var': [1, ... 0.000488 -4.5\n", + "847 {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ... 0.000488 -4.4\n", + "367 {'w': [0, 3, 0], 'budget_rule_slack_var': [1, ... 0.000977 -3.9\n", + "101 {'w': [1, 3, 1], 'budget_rule_slack_var': [1, ... 0.001465 -3.7\n", + "1023 {'w': [2, 1, 0], 'budget_rule_slack_var': [0, ... 0.000488 -3.5" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "from classiq.applications.combinatorial_optimization import (\n", - " get_optimization_solution_from_pyo,\n", - ")\n", - "\n", - "solution = get_optimization_solution_from_pyo(\n", - " portfolio_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n", - ")\n", - "optimization_result = pd.DataFrame.from_records(solution)\n", - "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)" + "optimization_result = combi.sample(optimized_params)\n", + "optimization_result.sort_values(by=\"cost\").head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "ac53b57d-8168-498e-ab04-e8dcbea1cfc1", + "metadata": {}, + "source": [ + "We will also want to compare the optimized results to uniformly sampled results:" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:19.845777Z", - "iopub.status.busy": "2024-05-07T15:06:19.845472Z", - "iopub.status.idle": "2024-05-07T15:06:19.849615Z", - "shell.execute_reply": "2024-05-07T15:06:19.848912Z" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x = [2, 1, 1] , cost = -4.800000000000001\n" - ] - } - ], + "execution_count": 11, + "id": "843cff7f-5230-4855-a1dc-13cbe386fa40", + "metadata": {}, + "outputs": [], "source": [ - "idx = optimization_result.cost.idxmin()\n", - "print(\n", - " \"x =\", optimization_result.solution[idx], \", cost =\", optimization_result.cost[idx]\n", - ")" + "uniform_result = combi.sample_uniform()" ] }, { "cell_type": "markdown", - "id": "b170b0ef-1e3f-4680-a7d4-82b4e537d785", + "id": "9c96cee5-1621-41b1-ace7-b3b59104acb4", "metadata": {}, "source": [ - "And the histogram:" + "And compare the histograms:" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "f48034c6-8000-4bcf-83b0-e3a76a38d9c7", + "execution_count": 12, + "id": "b6eaa101-6f7a-4497-a208-0b6a2d33416a", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:19.851995Z", - "iopub.status.busy": "2024-05-07T15:06:19.851643Z", - "iopub.status.idle": "2024-05-07T15:06:20.087635Z", - "shell.execute_reply": "2024-05-07T15:06:20.086888Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/plain": [ - "array([[]], dtype=object)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -598,7 +468,52 @@ } ], "source": [ - "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])" + "optimization_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=optimization_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"optimized\",\n", + ")\n", + "uniform_result[\"cost\"].plot(\n", + " kind=\"hist\",\n", + " bins=40,\n", + " edgecolor=\"black\",\n", + " weights=uniform_result[\"probability\"],\n", + " alpha=0.6,\n", + " label=\"uniform\",\n", + ")\n", + "plt.legend()\n", + "plt.ylabel(\"Probability\", fontsize=16)\n", + "plt.xlabel(\"cost\", fontsize=16)\n", + "plt.tick_params(axis=\"both\", labelsize=14)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x = [3, 1, 2] , cost = -4.6\n" + ] + } + ], + "source": [ + "best_solution = optimization_result.loc[optimization_result.cost.idxmin()]\n", + "print(\n", + " \"x =\",\n", + " best_solution.solution[\"w\"],\n", + " \", cost =\",\n", + " best_solution.cost,\n", + ")" ] }, { @@ -611,15 +526,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 43, "id": "324c9a09", "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T15:06:20.092527Z", - "iopub.status.busy": "2024-05-07T15:06:20.091284Z", - "iopub.status.idle": "2024-05-07T15:06:20.178692Z", - "shell.execute_reply": "2024-05-07T15:06:20.177959Z" - }, "pycharm": { "name": "#%%\n" }, @@ -634,20 +543,21 @@ "\n", " Variables:\n", " w : Size=3, Index=w_index\n", - " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : 1.9999999999999998 : 2 : False : False : Integers\n", - " 1 : 0 : 1.0000000000000002 : 2 : False : False : Integers\n", - " 2 : 0 : 1.0000000000000002 : 2 : False : False : Integers\n", + " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", + " 0 : 0 : 2.0 : 6 : False : False : Integers\n", + " 1 : 0 : 1.0 : 6 : False : False : Integers\n", + " 2 : 0 : 1.0 : 6 : False : False : Integers\n", "\n", " Objectives:\n", " cost : Size=1, Index=None, Active=True\n", " Key : Active : Value\n", - " None : True : -4.8\n", + " None : True : -4.800000000000001\n", "\n", " Constraints:\n", " budget_rule : Size=1\n", " Key : Lower : Body : Upper\n", - " None : None : 4.0 : 6.0\n" + " None : None : 4.0 : 6.0\n", + "Classical solution: [2, 1, 1]\n" ] } ], @@ -657,7 +567,11 @@ "solver = SolverFactory(\"couenne\")\n", "solver.solve(portfolio_model)\n", "\n", - "portfolio_model.display()" + "portfolio_model.display()\n", + "classical_solution = [\n", + " round(pyo.value(portfolio_model.w[i])) for i in range(len(portfolio_model.w))\n", + "]\n", + "print(\"Classical solution:\", classical_solution)" ] }, { @@ -704,7 +618,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.qmod b/applications/finance/portfolio_optimization/portfolio_optimization.qmod index f5008b88e..363c48bc6 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.qmod +++ b/applications/finance/portfolio_optimization/portfolio_optimization.qmod @@ -1,667 +1,17 @@ -hamiltonian: PauliTerm[] = [ - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=7.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=0.8 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=0.8 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=0.8 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.8 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.5 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=-0.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=0.65 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=0.65 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=0.65 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=0.65 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=0.9 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=0.9 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.9 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=0.9 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=2.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=1.25 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=3.0 - }, - PauliTerm { - pauli=[ - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=6.0 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z - ], - coefficient=1.45 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I - ], - coefficient=1.45 - }, - PauliTerm { - pauli=[ - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::Z, - Pauli::Z, - Pauli::I, - Pauli::I, - Pauli::I, - Pauli::I - ], - coefficient=1.45 - } -]; - -qfunc main(params_list: real[2], output target: qbit[9]) { - allocate(target.len, target); - qaoa_penalty(target.len, params_list, hamiltonian, target); +qstruct QAOAVars { + w: qnum<3, False, 0>[3]; + budget_rule_slack_var: qbit[3]; } -cscope ``` -vqe_result = vqe( -hamiltonian=hamiltonian, -maximize=False, -initial_point=[0.0, 0.2239532619279455], -optimizer=Optimizer.COBYLA, -max_iteration=60, -tolerance=0.0, -step_size=0.0, -skip_compute_variance=False, -alpha_cvar=0.7 -) -save({"vqe_result": vqe_result, "hamiltonian": hamiltonian}) -``` + +qfunc main(params: real[6], output v: QAOAVars) { + allocate(v.size, v); + hadamard_transform(v); + repeat (i: 3) { + phase (-(((((((v.w[0] * (((0.9 * v.w[0]) + (0.5 * v.w[1])) - (0.7 * v.w[2]))) - (3 * v.w[0])) + (v.w[1] * (((0.5 * v.w[0]) + (0.9 * v.w[1])) - (0.2 * v.w[2])))) - (4 * v.w[1])) + (v.w[2] * ((((-0.7) * v.w[0]) - (0.2 * v.w[1])) + (0.9 * v.w[2])))) + v.w[2]) + (360.0 * ((((((((0.1667 * v.budget_rule_slack_var[0]) + (0.3333 * v.budget_rule_slack_var[1])) + (0.5 * v.budget_rule_slack_var[2])) + (0.1667 * v.w[0])) + (0.1667 * v.w[1])) + (0.1667 * v.w[2])) - 1) ** 2))), params[i]); + apply_to_all(lambda(q) { + RX(params[3 + i], q); + }, v); + } +} diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json index 0967ef424..cf711f93e 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json +++ b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "tdg", + "z", + "u2", + "id", + "s", + "rz", + "sx", + "sdg", + "cy", + "x", + "cz", + "u", + "y", + "t", + "u1", + "rx", + "h", + "p", + "cx", + "r", + "ry", + "sxdg" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 3453328217 + } +} diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 93113b94e..49a59efc6 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -1,3 +1,4 @@ + 3_sat_grover.ipynb: 36 3_sat_grover.qmod: 48 3_sat_grover_large.qmod: 10 @@ -324,4 +325,4 @@ whitebox_fuzzing.ipynb: 720 whitebox_fuzzing.qmod: 720 X.qmod: 10 Yasir_Mansour_HW3_VQE.ipynb: 30 -Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 +Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 \ No newline at end of file From b9c70998f280f402c5c694f6114ace2bc6dc59cf Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Mon, 16 Dec 2024 15:07:52 +0200 Subject: [PATCH 21/38] fixed failing notebooks --- .../optimization/set_partition/set_partition.ipynb | 12 +++++++++++- tests/resources/timeouts.yaml | 7 +++---- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/applications/optimization/set_partition/set_partition.ipynb b/applications/optimization/set_partition/set_partition.ipynb index 2e8d0247c..a08237c9a 100644 --- a/applications/optimization/set_partition/set_partition.ipynb +++ b/applications/optimization/set_partition/set_partition.ipynb @@ -598,7 +598,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 40, + "id": "4a8215b7-1a7c-4f6c-ad8f-24754816038c", + "metadata": {}, + "outputs": [], + "source": [ + "classical_solution = [pyo.value(set_partition_model.x[i]) for i in range(len(mylist))]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da", "metadata": { "tags": [] diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index 49a59efc6..a461d6ace 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -1,4 +1,3 @@ - 3_sat_grover.ipynb: 36 3_sat_grover.qmod: 48 3_sat_grover_large.qmod: 10 @@ -147,8 +146,8 @@ max_independent_set.ipynb: 60 max_independent_set.qmod: 64 max_induced_k_color_subgraph.ipynb: 1028 max_induced_k_color_subgraph.qmod: 1008 -max_k_vertex_cover.ipynb: 300 -max_k_vertex_cover.qmod: 300 +max_k_vertex_cover.ipynb: 600 +max_k_vertex_cover.qmod: 600 maximum_float_example.qmod: 10 maximum_integer_example.qmod: 10 mcx.ipynb: 236 @@ -325,4 +324,4 @@ whitebox_fuzzing.ipynb: 720 whitebox_fuzzing.qmod: 720 X.qmod: 10 Yasir_Mansour_HW3_VQE.ipynb: 30 -Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 \ No newline at end of file +Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 From f1203654d6c2224b3d7e90f94760476337cf4626 Mon Sep 17 00:00:00 2001 From: ori-opher Date: Mon, 16 Dec 2024 18:15:54 +0200 Subject: [PATCH 22/38] Fix imports --- .../shor/shor_modular_exponentiation.ipynb | 8 ++--- ...Hisham_Mansour_HW4_qpe_for_molecules.ipynb | 36 +------------------ 2 files changed, 5 insertions(+), 39 deletions(-) diff --git a/algorithms/algebraic/shor/shor_modular_exponentiation.ipynb b/algorithms/algebraic/shor/shor_modular_exponentiation.ipynb index 30dbe04b2..203ed5548 100644 --- a/algorithms/algebraic/shor/shor_modular_exponentiation.ipynb +++ b/algorithms/algebraic/shor/shor_modular_exponentiation.ipynb @@ -175,7 +175,7 @@ "metadata": {}, "outputs": [], "source": [ - "from classiq.qmod import QNum, bind, control, within_apply\n", + "from classiq import *\n", "from classiq.qmod.builtins.classical_functions import qft_const_adder_phase\n", "\n", "\n", @@ -256,7 +256,7 @@ }, "outputs": [], "source": [ - "from classiq.qmod import QNum, inplace_prepare_int\n", + "from classiq import *\n", "\n", "modulo_num = 15\n", "reg_len = math.ceil(math.log(modulo_num, 2)) + 1\n", @@ -441,7 +441,7 @@ "metadata": {}, "outputs": [], "source": [ - "from classiq.qmod import SWAP, free\n", + "from classiq import *\n", "from classiq.qmod.symbolic import min, mod_inverse\n", "\n", "\n", @@ -535,7 +535,7 @@ "metadata": {}, "outputs": [], "source": [ - "from classiq.qmod import hadamard_transform\n", + "from classiq import *\n", "\n", "modulo_num = 6\n", "reg_len = math.ceil(math.log(modulo_num, 2)) + 1\n", diff --git a/community/QClass_2024/Submissions/HW4/Hisham_Mansour_HW4_qpe_for_molecules.ipynb b/community/QClass_2024/Submissions/HW4/Hisham_Mansour_HW4_qpe_for_molecules.ipynb index df5cc39bf..4108a8bdf 100644 --- a/community/QClass_2024/Submissions/HW4/Hisham_Mansour_HW4_qpe_for_molecules.ipynb +++ b/community/QClass_2024/Submissions/HW4/Hisham_Mansour_HW4_qpe_for_molecules.ipynb @@ -864,28 +864,7 @@ }, "outputs": [], "source": [ - "from classiq import molecule_problem_to_qmod\n", - "from classiq.qmod import (\n", - " CInt,\n", - " Output,\n", - " QArray,\n", - " QBit,\n", - " QCallable,\n", - " QNum,\n", - " allocate,\n", - " allocate_num,\n", - " control,\n", - " invert,\n", - " qfunc,\n", - " repeat,\n", - ")\n", - "from classiq.qmod.builtins import (\n", - " H,\n", - " apply_to_all,\n", - " exponentiation_with_depth_constraint,\n", - " molecule_hartree_fock,\n", - " qft,\n", - ")\n", + "from classiq import *\n", "from classiq.qmod.symbolic import log, pi\n", "\n", "# this constant will be multipled be a linear factor for each qbit of the qpe, so the\n", @@ -1410,19 +1389,6 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "9797b512-d41f-47c1-8c0f-4134f0500b80", - "metadata": { - "id": "9797b512-d41f-47c1-8c0f-4134f0500b80" - }, - "source": [ - "## References\n", - "\n", - "[1]: [Michael A. Nielsen and Isaac L. Chuang. 2011. Quantum Computation and Quantum Information: 10th Anniversary Edition, Cambridge University Press, New York, NY, USA.\n", - "](http://mmrc.amss.cas.cn/tlb/201702/W020170224608149940643.pdf)\n" - ] - }, { "cell_type": "markdown", "id": "ttDDvHfPDI4y", From 3756bc982c986294c51faf9999ff1fe439c422cf Mon Sep 17 00:00:00 2001 From: Ori Roth Date: Tue, 17 Dec 2024 13:26:45 +0200 Subject: [PATCH 23/38] Update qmods --- algorithms/dqi/dqi_max_xorsat.qmod | 90 +++++++++---------- .../dqi/dqi_max_xorsat.synthesis_options.json | 38 ++++---- .../grover/3_sat_grover/3_sat_grover.qmod | 2 +- .../3_sat_grover.synthesis_options.json | 41 ++++++++- .../3_sat_grover/3_sat_grover_large.qmod | 2 +- .../3_sat_grover_large.synthesis_options.json | 41 ++++++++- .../grover/grover_max_cut/grover_max_cut.qmod | 2 +- .../grover_max_cut.synthesis_options.json | 41 ++++++++- algorithms/hhl/hhl/hhl_exact.qmod | 12 +-- .../hhl/hhl/hhl_exact.synthesis_options.json | 31 +++---- algorithms/hhl/hhl/hhl_trotter.qmod | 8 +- .../hhl/hhl_trotter.synthesis_options.json | 31 +++---- .../portfolio_optimization.qmod | 2 - ...tfolio_optimization.synthesis_options.json | 32 +++---- .../integer_linear_programming.qmod | 2 - ..._linear_programming.synthesis_options.json | 32 +++---- .../optimization/max_clique/max_clique.qmod | 2 - .../max_clique.synthesis_options.json | 30 +++---- .../max_k_vertex_cover.qmod | 2 - .../max_k_vertex_cover.synthesis_options.json | 34 +++---- .../set_partition/set_partition.qmod | 4 +- .../set_partition.synthesis_options.json | 32 +++---- .../ising_model/ising_model.qmod | 2 - .../ising_model.synthesis_options.json | 32 +++---- 24 files changed, 325 insertions(+), 220 deletions(-) diff --git a/algorithms/dqi/dqi_max_xorsat.qmod b/algorithms/dqi/dqi_max_xorsat.qmod index 1db6c3d99..457376614 100644 --- a/algorithms/dqi/dqi_max_xorsat.qmod +++ b/algorithms/dqi/dqi_max_xorsat.qmod @@ -21,22 +21,22 @@ qfunc binary_to_one_hot_expanded___0(input binary: qnum<2, False, 0>, output one inplace_binary_to_one_hot_expanded___0(one_hot); } -qfunc iteration_lambda___0_0_expanded___0(qvar___3_captured__inplace_one_hot_to_unary__5: qbit, qvar___2_captured__inplace_one_hot_to_unary__5: qbit) { - CX(qvar___3_captured__inplace_one_hot_to_unary__5, qvar___2_captured__inplace_one_hot_to_unary__5); +qfunc iteration_0_lambda___0_0_expanded___0(qvar___3_captured__inplace_one_hot_to_unary__4: qbit, qvar___2_captured__inplace_one_hot_to_unary__4: qbit) { + CX(qvar___3_captured__inplace_one_hot_to_unary__4, qvar___2_captured__inplace_one_hot_to_unary__4); } -qfunc iteration_lambda___0_0_expanded___1(qvar___2_captured__inplace_one_hot_to_unary__5: qbit, qvar___1_captured__inplace_one_hot_to_unary__5: qbit) { - CX(qvar___2_captured__inplace_one_hot_to_unary__5, qvar___1_captured__inplace_one_hot_to_unary__5); +qfunc iteration_0_lambda___0_0_expanded___1(qvar___2_captured__inplace_one_hot_to_unary__4: qbit, qvar___1_captured__inplace_one_hot_to_unary__4: qbit) { + CX(qvar___2_captured__inplace_one_hot_to_unary__4, qvar___1_captured__inplace_one_hot_to_unary__4); } -qfunc iteration_lambda___0_0_expanded___2(qvar___1_captured__inplace_one_hot_to_unary__5: qbit, qvar___0_captured__inplace_one_hot_to_unary__5: qbit) { - CX(qvar___1_captured__inplace_one_hot_to_unary__5, qvar___0_captured__inplace_one_hot_to_unary__5); +qfunc iteration_0_lambda___0_0_expanded___2(qvar___1_captured__inplace_one_hot_to_unary__4: qbit, qvar___0_captured__inplace_one_hot_to_unary__4: qbit) { + CX(qvar___1_captured__inplace_one_hot_to_unary__4, qvar___0_captured__inplace_one_hot_to_unary__4); } qfunc inplace_one_hot_to_unary_expanded___0(qvar: qbit[4]) { - iteration_lambda___0_0_expanded___0(qvar[3], qvar[2]); - iteration_lambda___0_0_expanded___1(qvar[2], qvar[1]); - iteration_lambda___0_0_expanded___2(qvar[1], qvar[0]); + iteration_0_lambda___0_0_expanded___0(qvar[3], qvar[2]); + iteration_0_lambda___0_0_expanded___1(qvar[2], qvar[1]); + iteration_0_lambda___0_0_expanded___2(qvar[1], qvar[0]); X(qvar[0]); } @@ -165,37 +165,37 @@ qfunc prepare_dick_state_unary_input_expanded___5(qvar: qbit[6]) { prepare_dick_state_unary_input_expanded___4(qvar[1:6]); } -qfunc iteration_lambda___0_0_expanded___3(y___0_captured__vector_product_phase__3: qbit) { - Z(y___0_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___0(y___0_captured__vector_product_phase__2: qbit) { + Z(y___0_captured__vector_product_phase__2); } -qfunc iteration_lambda___0_0_expanded___4(y___1_captured__vector_product_phase__3: qbit) { - Z(y___1_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___1(y___1_captured__vector_product_phase__2: qbit) { + Z(y___1_captured__vector_product_phase__2); } -qfunc iteration_lambda___0_0_expanded___5(y___2_captured__vector_product_phase__3: qbit) { - Z(y___2_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___2(y___2_captured__vector_product_phase__2: qbit) { + Z(y___2_captured__vector_product_phase__2); } -qfunc iteration_lambda___0_0_expanded___6(y___3_captured__vector_product_phase__3: qbit) { - Z(y___3_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___3(y___3_captured__vector_product_phase__2: qbit) { + Z(y___3_captured__vector_product_phase__2); } -qfunc iteration_lambda___0_0_expanded___7(y___4_captured__vector_product_phase__3: qbit) { - Z(y___4_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___4(y___4_captured__vector_product_phase__2: qbit) { + Z(y___4_captured__vector_product_phase__2); } -qfunc iteration_lambda___0_0_expanded___8(y___5_captured__vector_product_phase__3: qbit) { - Z(y___5_captured__vector_product_phase__3); +qfunc iteration_1_lambda___0_0_expanded___5(y___5_captured__vector_product_phase__2: qbit) { + Z(y___5_captured__vector_product_phase__2); } qfunc vector_product_phase_expanded___0(y: qbit[6]) { - iteration_lambda___0_0_expanded___3(y[0]); - iteration_lambda___0_0_expanded___4(y[1]); - iteration_lambda___0_0_expanded___5(y[2]); - iteration_lambda___0_0_expanded___6(y[3]); - iteration_lambda___0_0_expanded___7(y[4]); - iteration_lambda___0_0_expanded___8(y[5]); + iteration_1_lambda___0_0_expanded___0(y[0]); + iteration_1_lambda___0_0_expanded___1(y[1]); + iteration_1_lambda___0_0_expanded___2(y[2]); + iteration_1_lambda___0_0_expanded___3(y[3]); + iteration_1_lambda___0_0_expanded___4(y[4]); + iteration_1_lambda___0_0_expanded___5(y[5]); } qfunc matrix_vector_product_expanded___0(y: qbit[6], output out: qbit[6]) { @@ -277,37 +277,37 @@ qfunc syndrome_decode_lookuptable_expanded___0(syndrome: qnum<6, False, 0>, erro } } -qfunc iteration_lambda___0_0_expanded___9(target___0_captured__apply_to_all__4: qbit) { - H(target___0_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___0(target___0_captured__apply_to_all__3: qbit) { + H(target___0_captured__apply_to_all__3); } -qfunc iteration_lambda___0_0_expanded___10(target___1_captured__apply_to_all__4: qbit) { - H(target___1_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___1(target___1_captured__apply_to_all__3: qbit) { + H(target___1_captured__apply_to_all__3); } -qfunc iteration_lambda___0_0_expanded___11(target___2_captured__apply_to_all__4: qbit) { - H(target___2_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___2(target___2_captured__apply_to_all__3: qbit) { + H(target___2_captured__apply_to_all__3); } -qfunc iteration_lambda___0_0_expanded___12(target___3_captured__apply_to_all__4: qbit) { - H(target___3_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___3(target___3_captured__apply_to_all__3: qbit) { + H(target___3_captured__apply_to_all__3); } -qfunc iteration_lambda___0_0_expanded___13(target___4_captured__apply_to_all__4: qbit) { - H(target___4_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___4(target___4_captured__apply_to_all__3: qbit) { + H(target___4_captured__apply_to_all__3); } -qfunc iteration_lambda___0_0_expanded___14(target___5_captured__apply_to_all__4: qbit) { - H(target___5_captured__apply_to_all__4); +qfunc iteration_2_lambda___0_0_expanded___5(target___5_captured__apply_to_all__3: qbit) { + H(target___5_captured__apply_to_all__3); } qfunc apply_to_all_expanded___0(target: qbit[6]) { - iteration_lambda___0_0_expanded___9(target[0]); - iteration_lambda___0_0_expanded___10(target[1]); - iteration_lambda___0_0_expanded___11(target[2]); - iteration_lambda___0_0_expanded___12(target[3]); - iteration_lambda___0_0_expanded___13(target[4]); - iteration_lambda___0_0_expanded___14(target[5]); + iteration_2_lambda___0_0_expanded___0(target[0]); + iteration_2_lambda___0_0_expanded___1(target[1]); + iteration_2_lambda___0_0_expanded___2(target[2]); + iteration_2_lambda___0_0_expanded___3(target[3]); + iteration_2_lambda___0_0_expanded___4(target[4]); + iteration_2_lambda___0_0_expanded___5(target[5]); } qfunc hadamard_transform_expanded___0(target: qbit[6]) { diff --git a/algorithms/dqi/dqi_max_xorsat.synthesis_options.json b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json index 1918752c6..ac599d9d2 100644 --- a/algorithms/dqi/dqi_max_xorsat.synthesis_options.json +++ b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json @@ -7,39 +7,37 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "z", + "rz", + "cx", + "h", "u1", - "p", - "cy", "r", - "id", - "y", - "sdg", - "x", - "u2", + "sx", "s", - "ry", - "cx", - "rz", - "h", + "rx", + "y", + "cy", "tdg", - "cz", + "ry", + "u2", "u", - "rx", + "x", + "cz", + "z", + "sxdg", + "sdg", "t", - "sx", - "sxdg" + "p", + "id" ], "is_symmetric_connectivity": true }, "debug_mode": true, "synthesize_all_separately": false, - "output_format": [ - "qasm" - ], + "output_format": ["qasm"], "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 3679731798 + "random_seed": -1 } } diff --git a/algorithms/grover/3_sat_grover/3_sat_grover.qmod b/algorithms/grover/3_sat_grover/3_sat_grover.qmod index cabfffd08..17f12c2f3 100644 --- a/algorithms/grover/3_sat_grover/3_sat_grover.qmod +++ b/algorithms/grover/3_sat_grover/3_sat_grover.qmod @@ -3,7 +3,7 @@ qfunc sat_oracle(x: qbit[], res: qbit) { } qfunc main(output x: qbit[3]) { - allocate(3, x); + allocate(x.len, x); grover_search(1, lambda(vars) { phase_oracle(sat_oracle, vars); }, x); diff --git a/algorithms/grover/3_sat_grover/3_sat_grover.synthesis_options.json b/algorithms/grover/3_sat_grover/3_sat_grover.synthesis_options.json index a20de7f23..a5609b34e 100644 --- a/algorithms/grover/3_sat_grover/3_sat_grover.synthesis_options.json +++ b/algorithms/grover/3_sat_grover/3_sat_grover.synthesis_options.json @@ -1,5 +1,44 @@ { "constraints": { - "max_width": 20 + "max_width": 20, + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "rz", + "cx", + "h", + "u1", + "r", + "sx", + "s", + "rx", + "y", + "cy", + "tdg", + "ry", + "u2", + "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", + "p", + "id" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": -1 } } diff --git a/algorithms/grover/3_sat_grover/3_sat_grover_large.qmod b/algorithms/grover/3_sat_grover/3_sat_grover_large.qmod index f6683d0bb..8f4a2dea4 100644 --- a/algorithms/grover/3_sat_grover/3_sat_grover_large.qmod +++ b/algorithms/grover/3_sat_grover/3_sat_grover_large.qmod @@ -3,7 +3,7 @@ qfunc sat_oracle_large(x: qbit[], res: qbit) { } qfunc main(output x: qbit[4]) { - allocate(4, x); + allocate(x.len, x); grover_search(2, lambda(vars) { phase_oracle(sat_oracle_large, vars); }, x); diff --git a/algorithms/grover/3_sat_grover/3_sat_grover_large.synthesis_options.json b/algorithms/grover/3_sat_grover/3_sat_grover_large.synthesis_options.json index b13c22eed..56d16ac17 100644 --- a/algorithms/grover/3_sat_grover/3_sat_grover_large.synthesis_options.json +++ b/algorithms/grover/3_sat_grover/3_sat_grover_large.synthesis_options.json @@ -1,5 +1,44 @@ { "constraints": { - "max_width": 24 + "max_width": 24, + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "rz", + "cx", + "h", + "u1", + "r", + "sx", + "s", + "rx", + "y", + "cy", + "tdg", + "ry", + "u2", + "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", + "p", + "id" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": -1 } } diff --git a/algorithms/grover/grover_max_cut/grover_max_cut.qmod b/algorithms/grover/grover_max_cut/grover_max_cut.qmod index 2285b0d20..a56250d91 100644 --- a/algorithms/grover/grover_max_cut/grover_max_cut.qmod +++ b/algorithms/grover/grover_max_cut/grover_max_cut.qmod @@ -3,7 +3,7 @@ qfunc cut_oracle(cut_size: int, nodes: qbit[], res: qbit) { } qfunc main(output nodes: qbit[5]) { - allocate(5, nodes); + allocate(nodes.len, nodes); grover_search(3, lambda(vars) { phase_oracle(lambda(vars, res) { cut_oracle(4, vars, res); diff --git a/algorithms/grover/grover_max_cut/grover_max_cut.synthesis_options.json b/algorithms/grover/grover_max_cut/grover_max_cut.synthesis_options.json index 589c0f316..bbef76503 100644 --- a/algorithms/grover/grover_max_cut/grover_max_cut.synthesis_options.json +++ b/algorithms/grover/grover_max_cut/grover_max_cut.synthesis_options.json @@ -1,5 +1,44 @@ { "constraints": { - "max_width": 22 + "max_width": 22, + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "rz", + "cx", + "h", + "u1", + "r", + "sx", + "s", + "rx", + "y", + "cy", + "tdg", + "ry", + "u2", + "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", + "p", + "id" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": -1 } } diff --git a/algorithms/hhl/hhl/hhl_exact.qmod b/algorithms/hhl/hhl/hhl_exact.qmod index 4d5d1f8a6..649f4e4a0 100644 --- a/algorithms/hhl/hhl/hhl_exact.qmod +++ b/algorithms/hhl/hhl/hhl_exact.qmod @@ -1,9 +1,3 @@ -qfunc unitary_with_power_logic(pw: int, matrix: real[][], target: qbit[]) { - power (pw) { - unitary(matrix, target); - } -} - qfunc load_b(amplitudes: real[], output state: qbit[], bound: real) { prepare_amplitudes(amplitudes, bound, state); } @@ -30,6 +24,12 @@ qfunc hhl(rhs_vector: real[], bound: real, precision: int, hamiltonian_evolution } } +qfunc unitary_with_power_logic(pw: int, matrix: real[][], target: qbit[]) { + power (pw) { + unitary(matrix, target); + } +} + qfunc main(output res: qnum, output phase: qnum, output indicator: qbit) { hhl([ 0.18257418583505536, diff --git a/algorithms/hhl/hhl/hhl_exact.synthesis_options.json b/algorithms/hhl/hhl/hhl_exact.synthesis_options.json index a89c3c20a..ba1113e93 100644 --- a/algorithms/hhl/hhl/hhl_exact.synthesis_options.json +++ b/algorithms/hhl/hhl/hhl_exact.synthesis_options.json @@ -7,32 +7,33 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "cy", "rz", - "z", - "sdg", - "t", - "s", + "cx", "h", - "u2", - "tdg", - "sx", - "ry", - "cz", "u1", - "y", "r", - "x", + "sx", + "s", "rx", - "sxdg", - "id", + "y", + "cy", + "tdg", + "ry", + "u2", "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", "p", - "cx" + "id" ], "is_symmetric_connectivity": true }, "debug_mode": true, + "synthesize_all_separately": false, "output_format": ["qasm"], "pretty_qasm": true, "transpilation_option": "auto optimize", diff --git a/algorithms/hhl/hhl/hhl_trotter.qmod b/algorithms/hhl/hhl/hhl_trotter.qmod index 0ee09ad5c..2a4387705 100644 --- a/algorithms/hhl/hhl/hhl_trotter.qmod +++ b/algorithms/hhl/hhl/hhl_trotter.qmod @@ -1,7 +1,3 @@ -qfunc suzuki_trotter1_with_power_logic(hamiltonian: PauliTerm[], pw: int, r0: int, reps_scaling_factor: real, evolution_coefficient: real, target: qbit[]) { - suzuki_trotter(hamiltonian, evolution_coefficient * pw, 1, r0 * ceiling(reps_scaling_factor ** log(pw, 2)), target); -} - qfunc load_b(amplitudes: real[], output state: qbit[], bound: real) { prepare_amplitudes(amplitudes, bound, state); } @@ -28,6 +24,10 @@ qfunc hhl(rhs_vector: real[], bound: real, precision: int, hamiltonian_evolution } } +qfunc suzuki_trotter1_with_power_logic(hamiltonian: PauliTerm[], pw: int, r0: int, reps_scaling_factor: real, evolution_coefficient: real, target: qbit[]) { + suzuki_trotter(hamiltonian, evolution_coefficient * pw, 1, r0 * ceiling(reps_scaling_factor ** log(pw, 2)), target); +} + qfunc main(output res: qnum, output phase: qnum, output indicator: qbit) { hhl([ 0.1825741858, diff --git a/algorithms/hhl/hhl/hhl_trotter.synthesis_options.json b/algorithms/hhl/hhl/hhl_trotter.synthesis_options.json index a89c3c20a..ba1113e93 100644 --- a/algorithms/hhl/hhl/hhl_trotter.synthesis_options.json +++ b/algorithms/hhl/hhl/hhl_trotter.synthesis_options.json @@ -7,32 +7,33 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "cy", "rz", - "z", - "sdg", - "t", - "s", + "cx", "h", - "u2", - "tdg", - "sx", - "ry", - "cz", "u1", - "y", "r", - "x", + "sx", + "s", "rx", - "sxdg", - "id", + "y", + "cy", + "tdg", + "ry", + "u2", "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", "p", - "cx" + "id" ], "is_symmetric_connectivity": true }, "debug_mode": true, + "synthesize_all_separately": false, "output_format": ["qasm"], "pretty_qasm": true, "transpilation_option": "auto optimize", diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.qmod b/applications/finance/portfolio_optimization/portfolio_optimization.qmod index 363c48bc6..bf1751dd0 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.qmod +++ b/applications/finance/portfolio_optimization/portfolio_optimization.qmod @@ -3,8 +3,6 @@ qstruct QAOAVars { budget_rule_slack_var: qbit[3]; } - - qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json index cf711f93e..ba1113e93 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json +++ b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "tdg", - "z", - "u2", - "id", - "s", "rz", + "cx", + "h", + "u1", + "r", "sx", - "sdg", + "s", + "rx", + "y", "cy", + "tdg", + "ry", + "u2", + "u", "x", "cz", - "u", - "y", + "z", + "sxdg", + "sdg", "t", - "u1", - "rx", - "h", "p", - "cx", - "r", - "ry", - "sxdg" + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 3453328217 + "random_seed": -1 } } diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod index 1b153d454..21489c8cf 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod @@ -4,8 +4,6 @@ qstruct QAOAVars { monotone_rule_2_slack_var_0: qbit; } - - qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json index 0ae05c835..ba1113e93 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "sx", - "tdg", - "z", "rz", - "y", + "cx", "h", - "cy", - "t", - "sdg", "u1", "r", - "id", - "cx", - "cz", + "sx", + "s", "rx", - "u", - "p", + "y", + "cy", + "tdg", "ry", - "sxdg", - "x", "u2", - "s" + "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", + "p", + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 2943844644 + "random_seed": -1 } } diff --git a/applications/optimization/max_clique/max_clique.qmod b/applications/optimization/max_clique/max_clique.qmod index bceade30c..413e3aba9 100644 --- a/applications/optimization/max_clique/max_clique.qmod +++ b/applications/optimization/max_clique/max_clique.qmod @@ -2,8 +2,6 @@ qstruct QAOAVars { x: qbit[7]; } - - qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); diff --git a/applications/optimization/max_clique/max_clique.synthesis_options.json b/applications/optimization/max_clique/max_clique.synthesis_options.json index bbb495713..ba1113e93 100644 --- a/applications/optimization/max_clique/max_clique.synthesis_options.json +++ b/applications/optimization/max_clique/max_clique.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "r", - "id", - "u", + "rz", "cx", - "sx", "h", - "y", "u1", + "r", + "sx", "s", - "u2", - "ry", - "rz", - "p", - "t", - "cz", "rx", - "sdg", + "y", "cy", - "x", "tdg", + "ry", + "u2", + "u", + "x", + "cz", + "z", "sxdg", - "z" + "sdg", + "t", + "p", + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 4176492311 + "random_seed": -1 } } diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod index dcd187633..98196497f 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.qmod @@ -2,8 +2,6 @@ qstruct QAOAVars { x: qbit[10]; } - - qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); diff --git a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json index 2d889877c..ba1113e93 100644 --- a/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json +++ b/applications/optimization/max_k_vertex_cover/max_k_vertex_cover.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "id", - "z", + "rz", + "cx", + "h", + "u1", + "r", "sx", - "sdg", "s", - "x", - "t", + "rx", "y", - "u1", - "cx", - "h", - "sxdg", - "cz", "cy", - "p", - "r", - "rz", - "ry", "tdg", - "rx", + "ry", "u2", - "u" + "u", + "x", + "cz", + "z", + "sxdg", + "sdg", + "t", + "p", + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 3857045423 + "random_seed": -1 } } diff --git a/applications/optimization/set_partition/set_partition.qmod b/applications/optimization/set_partition/set_partition.qmod index 703f5e6db..c24de2ab4 100644 --- a/applications/optimization/set_partition/set_partition.qmod +++ b/applications/optimization/set_partition/set_partition.qmod @@ -2,13 +2,11 @@ qstruct QAOAVars { x: qbit[10]; } - - qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 3) { - phase (-((((((((((((16 * v.x[0]) + (16 * v.x[1])) + (16 * v.x[2])) + (10 * v.x[3])) + (10 * v.x[4])) + (12 * v.x[5])) + (10 * v.x[6])) + (12 * v.x[7])) + (16 * v.x[8])) + (22 * v.x[9])) - 70) ** 2), params[i]); + phase (-((((((((((((4 * v.x[0]) + (16 * v.x[1])) + (14 * v.x[2])) + (18 * v.x[3])) + (16 * v.x[4])) + (16 * v.x[5])) + (12 * v.x[6])) + (4 * v.x[7])) + (10 * v.x[8])) + (20 * v.x[9])) - 65) ** 2), params[i]); apply_to_all(lambda(q) { RX(params[3 + i], q); }, v); diff --git a/applications/optimization/set_partition/set_partition.synthesis_options.json b/applications/optimization/set_partition/set_partition.synthesis_options.json index ac5cce353..ba1113e93 100644 --- a/applications/optimization/set_partition/set_partition.synthesis_options.json +++ b/applications/optimization/set_partition/set_partition.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "u", - "u1", + "rz", "cx", - "x", - "u2", - "sdg", - "s", + "h", + "u1", "r", - "cz", - "id", - "cy", - "sxdg", - "ry", + "sx", + "s", "rx", - "rz", "y", - "sx", + "cy", "tdg", - "t", + "ry", + "u2", + "u", + "x", + "cz", "z", + "sxdg", + "sdg", + "t", "p", - "h" + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 1468870560 + "random_seed": -1 } } diff --git a/applications/physical_systems/ising_model/ising_model.qmod b/applications/physical_systems/ising_model/ising_model.qmod index 6108af2c7..b2e4dca01 100644 --- a/applications/physical_systems/ising_model/ising_model.qmod +++ b/applications/physical_systems/ising_model/ising_model.qmod @@ -2,8 +2,6 @@ qstruct QAOAVars { z: qbit[6]; } - - qfunc main(params: real[10], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); diff --git a/applications/physical_systems/ising_model/ising_model.synthesis_options.json b/applications/physical_systems/ising_model/ising_model.synthesis_options.json index 9c23bd269..ba1113e93 100644 --- a/applications/physical_systems/ising_model/ising_model.synthesis_options.json +++ b/applications/physical_systems/ising_model/ising_model.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "u1", + "rz", "cx", - "p", - "cz", "h", - "x", + "u1", + "r", "sx", - "y", - "tdg", - "rz", - "cy", - "id", "s", - "r", - "t", - "z", "rx", - "u", - "sdg", + "y", + "cy", + "tdg", "ry", + "u2", + "u", + "x", + "cz", + "z", "sxdg", - "u2" + "sdg", + "t", + "p", + "id" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": 2916469319 + "random_seed": -1 } } From 198ab502774303ca18b71e9a771a4209c20bf1c6 Mon Sep 17 00:00:00 2001 From: Ori Roth Date: Tue, 17 Dec 2024 17:01:20 +0200 Subject: [PATCH 24/38] Update qmods --- .../discrete_poisson_solver.qmod | 8 ++--- .../oblivious_amplitude_amplification.qmod | 34 +++++++++---------- algorithms/simon/simon_example.qmod | 8 ++--- algorithms/simon/simon_shallow_example.qmod | 14 ++++---- algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod | 16 ++++----- .../whitebox_fuzzing/whitebox_fuzzing.qmod | 14 ++++---- .../option_pricing/option_pricing.qmod | 14 ++++---- .../quantum_walk_circle_balanced_coin.qmod | 20 +++++------ .../quantum_walk_hypercube.qmod | 8 ++--- .../hamiltonian_simulation_qubitization.qmod | 30 ++++++++-------- 10 files changed, 83 insertions(+), 83 deletions(-) diff --git a/algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.qmod b/algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.qmod index dbe6fd199..2b8392dd1 100644 --- a/algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.qmod +++ b/algorithms/differential_equations/discrete_poisson_solver/discrete_poisson_solver.qmod @@ -3,10 +3,6 @@ qfunc qsct_2d(xy_variable: qnum[2]) { qct_type2(xy_variable[1]); } -qfunc powered_hamiltonian_evolution(hamiltonian: PauliTerm[], scaling: real, p: int, qba: qbit[]) { - suzuki_trotter(hamiltonian, p * ((-6.28318530718) * scaling), 1, 1, qba); -} - qfunc inverse_amplitude_load(prefactor: real, phase: qnum, ind: qbit) { ind *= prefactor / phase; } @@ -22,6 +18,10 @@ qfunc matrix_inversion_HHL(prefactor: real, my_unitary: qfunc (int, qbit[]), sta } } +qfunc powered_hamiltonian_evolution(hamiltonian: PauliTerm[], scaling: real, p: int, qba: qbit[]) { + suzuki_trotter(hamiltonian, p * ((-6.28318530718) * scaling), 1, 1, qba); +} + qfunc main(output x_variable: qnum<3, False, 0>, output y_variable: qnum<3, False, 0>, output phase: qnum, output indicator: qbit) { xy_variable: qnum<3, False, 0>[2]; prepare_amplitudes([ diff --git a/algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.qmod b/algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.qmod index 154002cdf..a4b791895 100644 --- a/algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.qmod +++ b/algorithms/oblivious_amplitude_amplification/oblivious_amplitude_amplification.qmod @@ -1,3 +1,20 @@ +qfunc check_block(b: qnum, res: qbit) { + res ^= b == 0; +} + +qfunc oblivious_amplitude_amplification(reps: int, block_encoding: qfunc (qnum, qbit[]), block: qnum, data: qbit[]) { + block_encoding(data, block); + repeat (index: reps) { + grover_operator(lambda(b) { + phase_oracle(lambda(x, res) { + check_block(x, res); + }, b); + }, lambda(b) { + block_encoding(data, b); + }, block); + } +} + qfunc apply_pauli_term(pauli_string: Pauli[], x: qbit[]) { repeat (index: x.len) { switch(pauli_string[index], [lambda() { @@ -24,23 +41,6 @@ qfunc block_encode(pauli_list: Pauli[][], data: qbit[], block: qnum) { } } -qfunc check_block(b: qnum, res: qbit) { - res ^= b == 0; -} - -qfunc oblivious_amplitude_amplification(reps: int, block_encoding: qfunc (qnum, qbit[]), block: qnum, data: qbit[]) { - block_encoding(data, block); - repeat (index: reps) { - grover_operator(lambda(b) { - phase_oracle(lambda(x, res) { - check_block(x, res); - }, b); - }, lambda(b) { - block_encoding(data, b); - }, block); - } -} - qfunc main(output data: qnum, output block: qnum) { allocate(2, block); prepare_amplitudes([ diff --git a/algorithms/simon/simon_example.qmod b/algorithms/simon/simon_example.qmod index b14ec214a..81e82fd05 100644 --- a/algorithms/simon/simon_example.qmod +++ b/algorithms/simon/simon_example.qmod @@ -1,7 +1,3 @@ -qfunc simon_qfunc_simple(s: int, x: qnum, output res: qnum) { - res = min(x, x ^ s); -} - qfunc simon_qfunc(f_qfunc: qfunc (qnum, output qnum), x: qnum) { res: qnum; hadamard_transform(x); @@ -9,6 +5,10 @@ qfunc simon_qfunc(f_qfunc: qfunc (qnum, output qnum), x: qnum) { hadamard_transform(x); } +qfunc simon_qfunc_simple(s: int, x: qnum, output res: qnum) { + res = min(x, x ^ s); +} + qfunc main(output x: qnum) { allocate(5, x); simon_qfunc(lambda(x, res) { diff --git a/algorithms/simon/simon_shallow_example.qmod b/algorithms/simon/simon_shallow_example.qmod index 00268be57..903cf0120 100644 --- a/algorithms/simon/simon_shallow_example.qmod +++ b/algorithms/simon/simon_shallow_example.qmod @@ -1,3 +1,10 @@ +qfunc simon_qfunc(f_qfunc: qfunc (qnum, output qnum), x: qnum) { + res: qnum; + hadamard_transform(x); + f_qfunc(x, res); + hadamard_transform(x); +} + qfunc simon_qfunc_with_bipartite_s(partition_index: int, x: qbit[], output res: qbit[]) { allocate(x.len, res); repeat (i: x.len - partition_index) { @@ -9,13 +16,6 @@ qfunc simon_qfunc_with_bipartite_s(partition_index: int, x: qbit[], output res: } } -qfunc simon_qfunc(f_qfunc: qfunc (qnum, output qnum), x: qnum) { - res: qnum; - hadamard_transform(x); - f_qfunc(x, res); - hadamard_transform(x); -} - qfunc main(output x: qnum) { allocate(6, x); simon_qfunc(lambda(x, res) { diff --git a/algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod b/algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod index 8a0bb95f4..f0aeb7869 100644 --- a/algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod +++ b/algorithms/vqls/lcu_vqls/vqls_with_lcu.qmod @@ -1,3 +1,11 @@ +qfunc block_encoding_vqls(ansatz: qfunc (), block_encoding: qfunc (), prepare_b_state: qfunc ()) { + ansatz(); + block_encoding(); + invert { + prepare_b_state(); + } +} + qfunc apply_ry_on_all(params: real[], io: qbit[]) { repeat (index: io.len) { RY(params[index], io[index]); @@ -60,14 +68,6 @@ qfunc prepare_ca(pauli_terms_list: PauliTerm[], system_qubits: qbit[], ancillary } } -qfunc block_encoding_vqls(ansatz: qfunc (), block_encoding: qfunc (), prepare_b_state: qfunc ()) { - ansatz(); - block_encoding(); - invert { - prepare_b_state(); - } -} - qfunc main(params: real[9], output ancillary_qubits: qnum, output system_qubits: qnum) { allocate(2, ancillary_qubits); allocate(3, system_qubits); diff --git a/applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.qmod b/applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.qmod index 9a950049f..464ad58a1 100644 --- a/applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.qmod +++ b/applications/cybersecurity/whitebox_fuzzing/whitebox_fuzzing.qmod @@ -3,8 +3,9 @@ qfunc my_sp(x: qnum, y: qnum) { hadamard_transform(y); } -qfunc my_predicate(x: qnum, y: qnum, res: qbit) { - res ^= ((x + y) < 9) and (((x * y) % 4) == 1); +qfunc my_grover_operator(oracle_operand: qfunc (), diffuser_operand: qfunc ()) { + oracle_operand(); + diffuser_operand(); } qfunc prep_minus(output out: qbit) { @@ -22,6 +23,10 @@ qfunc my_oracle(predicate: qfunc (qbit)) { } } +qfunc my_predicate(x: qnum, y: qnum, res: qbit) { + res ^= ((x + y) < 9) and (((x * y) % 4) == 1); +} + qfunc zero_predicate(x: qnum, y: qnum, res: qbit) { joined: qnum; {x, y} -> joined; @@ -43,11 +48,6 @@ qfunc my_diffuser(sp_operand: qfunc (qnum, qnum), x: qnum, y: qnum) { } } -qfunc my_grover_operator(oracle_operand: qfunc (), diffuser_operand: qfunc ()) { - oracle_operand(); - diffuser_operand(); -} - qfunc main(output x: qnum, output y: qnum) { allocate_num(6, False, 0, x); allocate_num(6, False, 0, y); diff --git a/applications/finance/option_pricing/option_pricing.qmod b/applications/finance/option_pricing/option_pricing.qmod index 64fb81c4b..07dbfcdbd 100644 --- a/applications/finance/option_pricing/option_pricing.qmod +++ b/applications/finance/option_pricing/option_pricing.qmod @@ -3,6 +3,13 @@ qstruct OptionPricingState { ind: qbit; } +qfunc iqae_algorithm(k: int, oracle_operand: qfunc (qbit[]), sp_operand: qfunc (qbit[]), x: qbit[]) { + sp_operand(x); + power (k) { + grover_operator(oracle_operand, sp_operand, x); + } +} + qfunc iqae_oracle(state: OptionPricingState) { Z(state.ind); } @@ -59,13 +66,6 @@ qfunc european_call_state_preparation(state: OptionPricingState) { payoff(state.asset, state.ind); } -qfunc iqae_algorithm(k: int, oracle_operand: qfunc (qbit[]), sp_operand: qfunc (qbit[]), x: qbit[]) { - sp_operand(x); - power (k) { - grover_operator(oracle_operand, sp_operand, x); - } -} - qfunc main(k: int, output ind: qbit) { state: OptionPricingState; asset: qbit[]; diff --git a/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_circle_balanced_coin.qmod b/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_circle_balanced_coin.qmod index 6695b65b9..34d7eb0df 100644 --- a/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_circle_balanced_coin.qmod +++ b/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_circle_balanced_coin.qmod @@ -1,13 +1,3 @@ -qfunc quantum_step_clockwise(x: qbit[]) { - within { - qft(x); - } apply { - repeat (i: x.len) { - PHASE(((2 * pi) * (2 ** i)) / (2 ** x.len), x[i]); - } - } -} - qfunc discrete_quantum_walk(time: int, coin_flip_qfunc: qfunc (qnum), walks_qfuncs: qfunc[] (), coin_state: qnum) { power (time) { coin_flip_qfunc(coin_state); @@ -19,6 +9,16 @@ qfunc discrete_quantum_walk(time: int, coin_flip_qfunc: qfunc (qnum), walks_qfun } } +qfunc quantum_step_clockwise(x: qbit[]) { + within { + qft(x); + } apply { + repeat (i: x.len) { + PHASE(((2 * pi) * (2 ** i)) / (2 ** x.len), x[i]); + } + } +} + qfunc main(t: int, output x: qnum) { coin: qbit; allocate_num(floor(log(128, 2)), True, 0, x); diff --git a/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_hypercube.qmod b/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_hypercube.qmod index a47414f8a..5f78eec55 100644 --- a/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_hypercube.qmod +++ b/tutorials/advanced_tutorials/discrete_quantum_walk/quantum_walk_hypercube.qmod @@ -1,7 +1,3 @@ -qfunc moving_one_hamming_dist(pos: int, x: qbit[]) { - X(x[pos]); -} - qfunc discrete_quantum_walk(time: int, coin_flip_qfunc: qfunc (qnum), walks_qfuncs: qfunc[] (), coin_state: qnum) { power (time) { coin_flip_qfunc(coin_state); @@ -13,6 +9,10 @@ qfunc discrete_quantum_walk(time: int, coin_flip_qfunc: qfunc (qnum), walks_qfun } } +qfunc moving_one_hamming_dist(pos: int, x: qbit[]) { + X(x[pos]); +} + qfunc main(t: int, output x: qbit[]) { allocate(4, x); coin: qbit[]; diff --git a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qubitization.qmod b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qubitization.qmod index d08a6fbee..08fa45d93 100644 --- a/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qubitization.qmod +++ b/tutorials/popular_usage_examples/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_qubitization.qmod @@ -1,3 +1,18 @@ +qfunc lcu_cheb(coef: real[], generalized_signs: int[], walk_operator: qfunc (qnum, qbit[]), walk_block: qnum, walk_data: qbit[], cheb_block: qnum) { + within { + inplace_prepare_state(coef, 0.0, cheb_block); + } apply { + repeat (k: generalized_signs.len) { + control (cheb_block == k) { + U(0, 0, 0, (pi / 2) * generalized_signs[k], walk_data[0]); + power (k) { + walk_operator(walk_block, walk_data); + } + } + } + } +} + qfunc apply_pauli_term(pauli_string: PauliTerm, x: qbit[]) { repeat (index: x.len) { switch(pauli_string.pauli[index], [lambda() { @@ -52,21 +67,6 @@ qfunc my_walk_operator(block: qbit[], data: qbit[]) { RY(2 * pi, block[0]); } -qfunc lcu_cheb(coef: real[], generalized_signs: int[], walk_operator: qfunc (qnum, qbit[]), walk_block: qnum, walk_data: qbit[], cheb_block: qnum) { - within { - inplace_prepare_state(coef, 0.0, cheb_block); - } apply { - repeat (k: generalized_signs.len) { - control (cheb_block == k) { - U(0, 0, 0, (pi / 2) * generalized_signs[k], walk_data[0]); - power (k) { - walk_operator(walk_block, walk_data); - } - } - } - } -} - qfunc main(output ham_block: qnum, output data: qnum, output exp_block: qnum) { allocate(4, exp_block); allocate(2, ham_block); From 55ad73a2866c8d61cdaf6c32f5015e545264b501 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Wed, 18 Dec 2024 11:49:19 +0200 Subject: [PATCH 25/38] added missing timeouts --- tests/resources/timeouts.yaml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml index a461d6ace..57bea5e43 100644 --- a/tests/resources/timeouts.yaml +++ b/tests/resources/timeouts.yaml @@ -325,3 +325,11 @@ whitebox_fuzzing.qmod: 720 X.qmod: 10 Yasir_Mansour_HW3_VQE.ipynb: 30 Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600 +classiq_discrete_quantum_walk.ipynb: 100 +qiskit_discrete_quantum_walk.ipynb: 100 +tket_discrete_quantum_walk.ipynb: 100 +classiq_qsvt.ipynb: 100 +pennylane_cat_qsvt_example.ipynb: 100 +qiskit_qsvt.ipynb: 100 +pennylane_catalyst_discrete_quantum_walk.ipynb: 100 +tket_qsvt_example.ipynb: 100 From f44a2e40e36fd83a1a5b559e2fbc54e342c3ee5a Mon Sep 17 00:00:00 2001 From: Ori Roth Date: Wed, 18 Dec 2024 14:18:55 +0200 Subject: [PATCH 26/38] Update dqi Qmod --- algorithms/dqi/dqi_max_xorsat.qmod | 104 +++--------------- .../dqi/dqi_max_xorsat.synthesis_options.json | 36 +++--- 2 files changed, 33 insertions(+), 107 deletions(-) diff --git a/algorithms/dqi/dqi_max_xorsat.qmod b/algorithms/dqi/dqi_max_xorsat.qmod index 457376614..10cc1b098 100644 --- a/algorithms/dqi/dqi_max_xorsat.qmod +++ b/algorithms/dqi/dqi_max_xorsat.qmod @@ -21,27 +21,15 @@ qfunc binary_to_one_hot_expanded___0(input binary: qnum<2, False, 0>, output one inplace_binary_to_one_hot_expanded___0(one_hot); } -qfunc iteration_0_lambda___0_0_expanded___0(qvar___3_captured__inplace_one_hot_to_unary__4: qbit, qvar___2_captured__inplace_one_hot_to_unary__4: qbit) { - CX(qvar___3_captured__inplace_one_hot_to_unary__4, qvar___2_captured__inplace_one_hot_to_unary__4); -} - -qfunc iteration_0_lambda___0_0_expanded___1(qvar___2_captured__inplace_one_hot_to_unary__4: qbit, qvar___1_captured__inplace_one_hot_to_unary__4: qbit) { - CX(qvar___2_captured__inplace_one_hot_to_unary__4, qvar___1_captured__inplace_one_hot_to_unary__4); -} - -qfunc iteration_0_lambda___0_0_expanded___2(qvar___1_captured__inplace_one_hot_to_unary__4: qbit, qvar___0_captured__inplace_one_hot_to_unary__4: qbit) { - CX(qvar___1_captured__inplace_one_hot_to_unary__4, qvar___0_captured__inplace_one_hot_to_unary__4); -} - -qfunc inplace_one_hot_to_unary_expanded___0(qvar: qbit[4]) { - iteration_0_lambda___0_0_expanded___0(qvar[3], qvar[2]); - iteration_0_lambda___0_0_expanded___1(qvar[2], qvar[1]); - iteration_0_lambda___0_0_expanded___2(qvar[1], qvar[0]); +qfunc inplace_one_hot_to_unary(qvar: qbit[]) { + repeat (i: qvar.len - 1) { + CX(qvar[(qvar.len - i) - 1], qvar[(qvar.len - i) - 2]); + } X(qvar[0]); } qfunc one_hot_to_unary_expanded___0(input one_hot: qbit[4], output unary: qbit[3]) { - inplace_one_hot_to_unary_expanded___0(one_hot); + inplace_one_hot_to_unary(one_hot); lsb: qbit; one_hot -> {lsb, unary}; free(lsb); @@ -165,37 +153,12 @@ qfunc prepare_dick_state_unary_input_expanded___5(qvar: qbit[6]) { prepare_dick_state_unary_input_expanded___4(qvar[1:6]); } -qfunc iteration_1_lambda___0_0_expanded___0(y___0_captured__vector_product_phase__2: qbit) { - Z(y___0_captured__vector_product_phase__2); -} - -qfunc iteration_1_lambda___0_0_expanded___1(y___1_captured__vector_product_phase__2: qbit) { - Z(y___1_captured__vector_product_phase__2); -} - -qfunc iteration_1_lambda___0_0_expanded___2(y___2_captured__vector_product_phase__2: qbit) { - Z(y___2_captured__vector_product_phase__2); -} - -qfunc iteration_1_lambda___0_0_expanded___3(y___3_captured__vector_product_phase__2: qbit) { - Z(y___3_captured__vector_product_phase__2); -} - -qfunc iteration_1_lambda___0_0_expanded___4(y___4_captured__vector_product_phase__2: qbit) { - Z(y___4_captured__vector_product_phase__2); -} - -qfunc iteration_1_lambda___0_0_expanded___5(y___5_captured__vector_product_phase__2: qbit) { - Z(y___5_captured__vector_product_phase__2); -} - -qfunc vector_product_phase_expanded___0(y: qbit[6]) { - iteration_1_lambda___0_0_expanded___0(y[0]); - iteration_1_lambda___0_0_expanded___1(y[1]); - iteration_1_lambda___0_0_expanded___2(y[2]); - iteration_1_lambda___0_0_expanded___3(y[3]); - iteration_1_lambda___0_0_expanded___4(y[4]); - iteration_1_lambda___0_0_expanded___5(y[5]); +qfunc vector_product_phase(v: int[], y: qbit[]) { + repeat (i: y.len) { + if (v[i] > 0) { + Z(y[i]); + } + } } qfunc matrix_vector_product_expanded___0(y: qbit[6], output out: qbit[6]) { @@ -208,7 +171,7 @@ qfunc matrix_vector_product_expanded___0(y: qbit[6], output out: qbit[6]) { out[5] ^= (0 ^ y[4]) ^ y[5]; } -qfunc syndrome_decode_lookuptable_expanded___0(syndrome: qnum<6, False, 0>, error: qnum<6, False, 0>) { +qfunc syndrome_decode_lookuptable(syndrome: qnum, error: qnum) { control (syndrome == 0) { error ^= 0; } @@ -277,43 +240,6 @@ qfunc syndrome_decode_lookuptable_expanded___0(syndrome: qnum<6, False, 0>, erro } } -qfunc iteration_2_lambda___0_0_expanded___0(target___0_captured__apply_to_all__3: qbit) { - H(target___0_captured__apply_to_all__3); -} - -qfunc iteration_2_lambda___0_0_expanded___1(target___1_captured__apply_to_all__3: qbit) { - H(target___1_captured__apply_to_all__3); -} - -qfunc iteration_2_lambda___0_0_expanded___2(target___2_captured__apply_to_all__3: qbit) { - H(target___2_captured__apply_to_all__3); -} - -qfunc iteration_2_lambda___0_0_expanded___3(target___3_captured__apply_to_all__3: qbit) { - H(target___3_captured__apply_to_all__3); -} - -qfunc iteration_2_lambda___0_0_expanded___4(target___4_captured__apply_to_all__3: qbit) { - H(target___4_captured__apply_to_all__3); -} - -qfunc iteration_2_lambda___0_0_expanded___5(target___5_captured__apply_to_all__3: qbit) { - H(target___5_captured__apply_to_all__3); -} - -qfunc apply_to_all_expanded___0(target: qbit[6]) { - iteration_2_lambda___0_0_expanded___0(target[0]); - iteration_2_lambda___0_0_expanded___1(target[1]); - iteration_2_lambda___0_0_expanded___2(target[2]); - iteration_2_lambda___0_0_expanded___3(target[3]); - iteration_2_lambda___0_0_expanded___4(target[4]); - iteration_2_lambda___0_0_expanded___5(target[5]); -} - -qfunc hadamard_transform_expanded___0(target: qbit[6]) { - apply_to_all_expanded___0(target); -} - qfunc dqi_max_xor_sat_expanded___0(output y: qbit[6], output solution: qbit[6]) { k_num_errors: qnum<2, False, 0>; prepare_amplitudes([ @@ -326,10 +252,10 @@ qfunc dqi_max_xor_sat_expanded___0(output y: qbit[6], output solution: qbit[6]) binary_to_unary_expanded___0(k_num_errors, k_unary); pad_zeros_expanded___0(k_unary, y); prepare_dick_state_unary_input_expanded___5(y); - vector_product_phase_expanded___0(y); + vector_product_phase([1.0, 1.0, 1.0, 1.0, 1.0, 1.0], y); matrix_vector_product_expanded___0(y, solution); - syndrome_decode_lookuptable_expanded___0(solution, y); - hadamard_transform_expanded___0(solution); + syndrome_decode_lookuptable(solution, y); + hadamard_transform(solution); } qfunc main(output y: qbit[6], output solution: qbit[6]) { diff --git a/algorithms/dqi/dqi_max_xorsat.synthesis_options.json b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json index ac599d9d2..f8d613bfd 100644 --- a/algorithms/dqi/dqi_max_xorsat.synthesis_options.json +++ b/algorithms/dqi/dqi_max_xorsat.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "rz", - "cx", - "h", - "u1", - "r", - "sx", - "s", - "rx", - "y", - "cy", - "tdg", - "ry", "u2", - "u", - "x", - "cz", + "cy", + "rx", + "id", "z", + "u", "sxdg", - "sdg", + "x", + "rz", + "sx", + "ry", "t", "p", - "id" + "u1", + "y", + "tdg", + "s", + "h", + "r", + "cx", + "sdg", + "cz" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": -1 + "random_seed": 2676057990 } } From f5de9ca17688372f48caadb071a061d66669f585 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Wed, 18 Dec 2024 18:46:30 +0200 Subject: [PATCH 27/38] adjusted integer using notebooks --- .../portfolio_optimization.ipynb | 68 ++++++------ ...tfolio_optimization.synthesis_options.json | 34 +++--- .../integer_linear_programming.ipynb | 105 ++++++++++-------- .../integer_linear_programming.qmod | 6 +- ..._linear_programming.synthesis_options.json | 32 +++--- 5 files changed, 127 insertions(+), 118 deletions(-) diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb index 5fff5f122..75687a310 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb +++ b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb @@ -121,7 +121,7 @@ "num_assets = len(returns)\n", "\n", "# setting the variables\n", - "portfolio_model.w = pyo.Var(range(num_assets), domain=pyo.Integers, bounds=(0, 6))\n", + "portfolio_model.w = pyo.Var(range(num_assets), domain=pyo.Integers, bounds=(0, 7))\n", "\n", "w_array = list(portfolio_model.w.values())\n", "\n", @@ -202,7 +202,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/79e25a43-a916-476c-a108-022dfec85da8?version=0.62.0.dev9\n" + "Opening: https://nightly.platform.classiq.io/circuit/83bbd639-d2b0-4fb6-b89b-b9a790a5bd4d?version=0.63.0.dev2\n" ] } ], @@ -253,7 +253,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Optimization Progress: 61it [04:44, 4.67s/it] \n" + "Optimization Progress: 61it [05:10, 5.09s/it] \n" ] } ], @@ -290,7 +290,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -370,34 +370,34 @@ " \n", " \n", " \n", - " 1022\n", - " {'w': [1, 2, 0], 'budget_rule_slack_var': [1, ...\n", - " 0.000488\n", - " -4.5\n", + " 180\n", + " {'w': [1, 2, 1], 'budget_rule_slack_var': [0, ...\n", + " 0.000977\n", + " -4.8\n", " \n", " \n", - " 847\n", - " {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ...\n", + " 515\n", + " {'w': [3, 1, 2], 'budget_rule_slack_var': [0, ...\n", " 0.000488\n", - " -4.4\n", + " -4.6\n", " \n", " \n", - " 367\n", - " {'w': [0, 3, 0], 'budget_rule_slack_var': [1, ...\n", - " 0.000977\n", - " -3.9\n", + " 112\n", + " {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ...\n", + " 0.001465\n", + " -4.4\n", " \n", " \n", - " 101\n", - " {'w': [1, 3, 1], 'budget_rule_slack_var': [1, ...\n", - " 0.001465\n", - " -3.7\n", + " 713\n", + " {'w': [2, 1, 2], 'budget_rule_slack_var': [1, ...\n", + " 0.000488\n", + " -4.3\n", " \n", " \n", - " 1023\n", - " {'w': [2, 1, 0], 'budget_rule_slack_var': [0, ...\n", + " 1285\n", + " {'w': [1, 1, 0], 'budget_rule_slack_var': [1, ...\n", " 0.000488\n", - " -3.5\n", + " -4.2\n", " \n", " \n", "\n", @@ -405,11 +405,11 @@ ], "text/plain": [ " solution probability cost\n", - "1022 {'w': [1, 2, 0], 'budget_rule_slack_var': [1, ... 0.000488 -4.5\n", - "847 {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ... 0.000488 -4.4\n", - "367 {'w': [0, 3, 0], 'budget_rule_slack_var': [1, ... 0.000977 -3.9\n", - "101 {'w': [1, 3, 1], 'budget_rule_slack_var': [1, ... 0.001465 -3.7\n", - "1023 {'w': [2, 1, 0], 'budget_rule_slack_var': [0, ... 0.000488 -3.5" + "180 {'w': [1, 2, 1], 'budget_rule_slack_var': [0, ... 0.000977 -4.8\n", + "515 {'w': [3, 1, 2], 'budget_rule_slack_var': [0, ... 0.000488 -4.6\n", + "112 {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ... 0.001465 -4.4\n", + "713 {'w': [2, 1, 2], 'budget_rule_slack_var': [1, ... 0.000488 -4.3\n", + "1285 {'w': [1, 1, 0], 'budget_rule_slack_var': [1, ... 0.000488 -4.2" ] }, "execution_count": 10, @@ -458,7 +458,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 13, "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877", "metadata": { "tags": [] @@ -502,7 +502,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "x = [3, 1, 2] , cost = -4.6\n" + "x = [1, 2, 1] , cost = -4.8\n" ] } ], @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 14, "id": "324c9a09", "metadata": { "pycharm": { @@ -544,9 +544,9 @@ " Variables:\n", " w : Size=3, Index=w_index\n", " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : 2.0 : 6 : False : False : Integers\n", - " 1 : 0 : 1.0 : 6 : False : False : Integers\n", - " 2 : 0 : 1.0 : 6 : False : False : Integers\n", + " 0 : 0 : 2.0 : 7 : False : False : Integers\n", + " 1 : 0 : 1.0 : 7 : False : False : Integers\n", + " 2 : 0 : 1.0 : 7 : False : False : Integers\n", "\n", " Objectives:\n", " cost : Size=1, Index=None, Active=True\n", diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json index ba1113e93..8e4d13d71 100644 --- a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json +++ b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "rz", - "cx", + "p", + "cz", "h", + "t", + "rz", "u1", - "r", - "sx", - "s", - "rx", - "y", - "cy", - "tdg", + "z", "ry", + "sxdg", + "cx", + "rx", "u2", + "cy", "u", - "x", - "cz", - "z", - "sxdg", + "y", + "sx", + "r", + "s", + "tdg", + "id", "sdg", - "t", - "p", - "id" + "x" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": -1 + "random_seed": 4243313233 } } diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb index 702813b48..e5dafe97a 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "48889b21-557b-481c-80c5-3c0b5c91adb6", "metadata": { "ExecuteTime": { @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "6e5295f4-7ba6-4ff6-8782-1c4c2c7f85e4", "metadata": { "ExecuteTime": { @@ -116,12 +116,12 @@ "c = np.array([1, 2, 3])\n", "\n", "# Instantiate the model\n", - "ilp_model = ilp(A, b, c, 4)" + "ilp_model = ilp(A, b, c, 3)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "345330b2-9c14-41f6-b4ba-e11fb9ca1565", "metadata": { "ExecuteTime": { @@ -146,9 +146,9 @@ "1 Var Declarations\n", " x : Size=3, Index=x_index\n", " Key : Lower : Value : Upper : Fixed : Stale : Domain\n", - " 0 : 0 : None : 4 : False : True : NonNegativeIntegers\n", - " 1 : 0 : None : 4 : False : True : NonNegativeIntegers\n", - " 2 : 0 : None : 4 : False : True : NonNegativeIntegers\n", + " 0 : 0 : None : 3 : False : True : NonNegativeIntegers\n", + " 1 : 0 : None : 3 : False : True : NonNegativeIntegers\n", + " 2 : 0 : None : 3 : False : True : NonNegativeIntegers\n", "\n", "1 Objective Declarations\n", " cost : Size=1, Index=None, Active=True\n", @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "816b468f-a59f-4f2f-8337-4a9d66548425", "metadata": { "tags": [] @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "62ec28b3-cb49-411a-8c4a-8004fff6c105", "metadata": {}, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31", "metadata": { "pycharm": { @@ -234,7 +234,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://nightly.platform.classiq.io/circuit/ef888bc5-02f8-4150-b49f-76cd8bbc7ecf?version=0.62.0.dev9\n" + "Opening: https://nightly.platform.classiq.io/circuit/2d391a9b-4f75-4b90-ba76-fe6eb0cc3dec?version=0.63.0.dev2\n" ] } ], @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "7d188c69-21d1-4afe-86b1-46229e91a01e", "metadata": {}, "outputs": [], @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40", "metadata": { "tags": [] @@ -285,7 +285,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Optimization Progress: 72%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 65/90 [03:27<01:19, 3.19s/it]\n" + "Optimization Progress: 76%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 68/90 [02:24<00:46, 2.12s/it]\n" ] } ], @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "02454398-b229-403c-824a-b1eb539fbc1f", "metadata": { "tags": [] @@ -315,13 +315,13 @@ "Text(0.5, 1.0, 'Cost convergence')" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "9638f749-a60b-4176-a4ea-50d7c2bb986f", "metadata": {}, "outputs": [ @@ -399,49 +399,56 @@ " \n", " \n", " \n", - " 32\n", - " {'x': [0, 0, 1]}\n", - " 0.004883\n", + " 18\n", + " {'x': [0, 0, 1], 'monotone_rule_1_slack_var': ...\n", + " 0.011719\n", " -3.000000e+00\n", " \n", " \n", " 0\n", - " {'x': [0, 1, 0]}\n", - " 0.281738\n", + " {'x': [0, 1, 0], 'monotone_rule_1_slack_var': ...\n", + " 0.030762\n", " -2.000000e+00\n", " \n", " \n", - " 8\n", - " {'x': [1, 0, 0]}\n", - " 0.016113\n", - " -1.000000e+00\n", + " 9\n", + " {'x': [0, 0, 0], 'monotone_rule_1_slack_var': ...\n", + " 0.018066\n", + " 1.527468e-150\n", " \n", " \n", - " 73\n", - " {'x': [0, 0, 0]}\n", + " 150\n", + " {'x': [0, 0, 1], 'monotone_rule_1_slack_var': ...\n", " 0.001465\n", - " 1.527468e-150\n", + " 7.000000e+00\n", " \n", " \n", - " 104\n", - " {'x': [0, 0, 1]}\n", - " 0.000977\n", - " 7.000000e+00\n", + " 130\n", + " {'x': [0, 1, 0], 'monotone_rule_1_slack_var': ...\n", + " 0.001953\n", + " 8.000000e+00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " solution probability cost\n", - "32 {'x': [0, 0, 1]} 0.004883 -3.000000e+00\n", - "0 {'x': [0, 1, 0]} 0.281738 -2.000000e+00\n", - "8 {'x': [1, 0, 0]} 0.016113 -1.000000e+00\n", - "73 {'x': [0, 0, 0]} 0.001465 1.527468e-150\n", - "104 {'x': [0, 0, 1]} 0.000977 7.000000e+00" + " solution probability \\\n", + "18 {'x': [0, 0, 1], 'monotone_rule_1_slack_var': ... 0.011719 \n", + "0 {'x': [0, 1, 0], 'monotone_rule_1_slack_var': ... 0.030762 \n", + "9 {'x': [0, 0, 0], 'monotone_rule_1_slack_var': ... 0.018066 \n", + "150 {'x': [0, 0, 1], 'monotone_rule_1_slack_var': ... 0.001465 \n", + "130 {'x': [0, 1, 0], 'monotone_rule_1_slack_var': ... 0.001953 \n", + "\n", + " cost \n", + "18 -3.000000e+00 \n", + "0 -2.000000e+00 \n", + "9 1.527468e-150 \n", + "150 7.000000e+00 \n", + "130 8.000000e+00 " ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -461,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "25277397-d7a5-4466-af4c-fee2e17bc8b9", "metadata": {}, "outputs": [], @@ -479,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "77668ce1-64f6-4086-b0fe-597ded8ad0e0", "metadata": { "tags": [] @@ -487,7 +494,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -529,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c", "metadata": { "tags": [] @@ -538,10 +545,12 @@ { "data": { "text/plain": [ - "{'x': [0, 0, 1]}" + "{'x': [0, 0, 1],\n", + " 'monotone_rule_1_slack_var': [0],\n", + " 'monotone_rule_2_slack_var': [0]}" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -569,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57", "metadata": { "pycharm": { diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod index 21489c8cf..d447b9a52 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.qmod +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.qmod @@ -1,14 +1,14 @@ qstruct QAOAVars { x: qnum<2, False, 0>[3]; - monotone_rule_1_slack_var_0: qbit; - monotone_rule_2_slack_var_0: qbit; + monotone_rule_1_slack_var: qbit[1]; + monotone_rule_2_slack_var: qbit[1]; } qfunc main(params: real[6], output v: QAOAVars) { allocate(v.size, v); hadamard_transform(v); repeat (i: 3) { - phase (-(((((((((10 * v.x[0]) * v.x[1]) + ((10 * v.x[0]) * v.x[2])) - v.x[0]) + ((10 * v.x[1]) * v.x[2])) - (2 * v.x[1])) - (3 * v.x[2])) + (10 * (((((v.monotone_rule_1_slack_var_0 + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))) + (10 * (((((v.monotone_rule_2_slack_var_0 + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))), params[i]); + phase (-(((((((((10 * v.x[0]) * v.x[1]) + ((10 * v.x[0]) * v.x[2])) - v.x[0]) + ((10 * v.x[1]) * v.x[2])) - (2 * v.x[1])) - (3 * v.x[2])) + (10 * (((((v.monotone_rule_1_slack_var[0] + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))) + (10 * (((((v.monotone_rule_2_slack_var[0] + v.x[0]) + v.x[1]) + v.x[2]) - 1.0) ** 2))), params[i]); apply_to_all(lambda(q) { RX(params[3 + i], q); }, v); diff --git a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json index ba1113e93..0523412fc 100644 --- a/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json +++ b/applications/optimization/integer_linear_programming/integer_linear_programming.synthesis_options.json @@ -7,28 +7,28 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ + "t", "rz", - "cx", - "h", - "u1", - "r", "sx", - "s", - "rx", - "y", "cy", + "rx", + "cz", "tdg", - "ry", - "u2", "u", - "x", - "cz", - "z", - "sxdg", + "ry", + "y", + "u1", + "h", "sdg", - "t", + "cx", + "r", + "sxdg", + "x", + "id", "p", - "id" + "z", + "u2", + "s" ], "is_symmetric_connectivity": true }, @@ -38,6 +38,6 @@ "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": -1 + "random_seed": 3739250181 } } From bdc84d8abbb8268813492c806f8609a5d7856704 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Thu, 19 Dec 2024 12:00:35 +0200 Subject: [PATCH 28/38] test commit Co-authored-by: Lior Preminger --- algorithms/algebraic/hidden_shift/hidden_shift.ipynb | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/algorithms/algebraic/hidden_shift/hidden_shift.ipynb b/algorithms/algebraic/hidden_shift/hidden_shift.ipynb index 5f106413b..c67139271 100644 --- a/algorithms/algebraic/hidden_shift/hidden_shift.ipynb +++ b/algorithms/algebraic/hidden_shift/hidden_shift.ipynb @@ -63,9 +63,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "" - ] + "text": [] } ], "source": [ @@ -253,8 +251,7 @@ "output_type": "stream", "text": [ "f_dual: (((((((((x[5]) & (y[0])) ^ ((x[2]) & (y[1]))) ^ ((x[7]) & (y[2]))) ^ ((x[0]) & (y[3]))) ^ ((x[6]) & (y[4]))) ^ ((x[3]) & (y[5]))) ^ ((x[1]) & (y[6]))) ^ ((x[4]) & (y[7]))) ^ ((((((((x[5]) & (x[2])) & (x[7])) & (x[0])) & (x[6])) & (x[3])) & (x[1])) & (x[4]))\n", - "g: (((((((((x[0]) & (y[3])) ^ (((x[1]) ^ 1) & (y[6]))) ^ ((x[2]) & ((y[1]) ^ 1))) ^ (((x[3]) ^ 1) & (y[5]))) ^ ((x[4]) & (y[7]))) ^ ((x[5]) & (y[0]))) ^ ((x[6]) & (y[4]))) ^ ((x[7]) & (y[2]))) ^ ((((((((y[0]) & ((y[1]) ^ 1)) & (y[2])) & (y[3])) & (y[4])) & (y[5])) & (y[6])) & (y[7]))\n", - "" + "g: (((((((((x[0]) & (y[3])) ^ (((x[1]) ^ 1) & (y[6]))) ^ ((x[2]) & ((y[1]) ^ 1))) ^ (((x[3]) ^ 1) & (y[5]))) ^ ((x[4]) & (y[7]))) ^ ((x[5]) & (y[0]))) ^ ((x[6]) & (y[4]))) ^ ((x[7]) & (y[2]))) ^ ((((((((y[0]) & ((y[1]) ^ 1)) & (y[2])) & (y[3])) & (y[4])) & (y[5])) & (y[6])) & (y[7]))\n" ] } ], @@ -389,8 +386,7 @@ "output_type": "stream", "text": [ "f: (((((((((x[0]) & (y[3])) ^ ((x[1]) & (y[6]))) ^ ((x[2]) & (y[1]))) ^ ((x[3]) & (y[5]))) ^ ((x[4]) & (y[7]))) ^ ((x[5]) & (y[0]))) ^ ((x[6]) & (y[4]))) ^ ((x[7]) & (y[2]))) ^ ((((((((y[0]) & (y[1])) & (y[2])) & (y[3])) & (y[4])) & (y[5])) & (y[6])) & (y[7]))\n", - "g: (((((((((x[0]) & (y[3])) ^ (((x[1]) ^ 1) & (y[6]))) ^ ((x[2]) & ((y[1]) ^ 1))) ^ (((x[3]) ^ 1) & (y[5]))) ^ ((x[4]) & (y[7]))) ^ ((x[5]) & (y[0]))) ^ ((x[6]) & (y[4]))) ^ ((x[7]) & (y[2]))) ^ ((((((((y[0]) & ((y[1]) ^ 1)) & (y[2])) & (y[3])) & (y[4])) & (y[5])) & (y[6])) & (y[7]))\n", - "" + "g: (((((((((x[0]) & (y[3])) ^ (((x[1]) ^ 1) & (y[6]))) ^ ((x[2]) & ((y[1]) ^ 1))) ^ (((x[3]) ^ 1) & (y[5]))) ^ ((x[4]) & (y[7]))) ^ ((x[5]) & (y[0]))) ^ ((x[6]) & (y[4]))) ^ ((x[7]) & (y[2]))) ^ ((((((((y[0]) & ((y[1]) ^ 1)) & (y[2])) & (y[3])) & (y[4])) & (y[5])) & (y[6])) & (y[7]))\n" ] } ], @@ -589,7 +585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.4" } }, "nbformat": 4, From 3be616154fdf3b0c87cf8d812f11db9e319b862f Mon Sep 17 00:00:00 2001 From: Dor Harpaz Date: Thu, 19 Dec 2024 13:50:22 +0200 Subject: [PATCH 29/38] Fix file rename --- .github/workflows/Internal-automatic-deployment.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/Internal-automatic-deployment.yml b/.github/workflows/Internal-automatic-deployment.yml index 936acfa73..a3abce596 100644 --- a/.github/workflows/Internal-automatic-deployment.yml +++ b/.github/workflows/Internal-automatic-deployment.yml @@ -11,14 +11,14 @@ on: jobs: deploy-prod: if: github.event.pull_request.merged == true && github.event.pull_request.base.ref == 'main' - uses: ./.github/workflows/deployment-qmod.yml + uses: ./.github/workflows/Utils-deployment-qmod.yml with: deploy-mode: production secrets: inherit deploy-dev: if: github.event.pull_request.merged == true && github.event.pull_request.base.ref == 'dev' - uses: ./.github/workflows/deployment-qmod.yml + uses: ./.github/workflows/Utils-deployment-qmod.yml with: deploy-mode: staging secrets: inherit From a5c46dcc8373ea1a575a4038097d60e8d7f8fa7f Mon Sep 17 00:00:00 2001 From: Nadav Ben Ami Date: Thu, 19 Dec 2024 17:04:05 +0200 Subject: [PATCH 30/38] adding SKEAPED_URLS list --- tests/test_links.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/test_links.py b/tests/test_links.py index facec93a9..609853f35 100644 --- a/tests/test_links.py +++ b/tests/test_links.py @@ -12,6 +12,9 @@ URL_REGEX = r"https?:\/\/[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b[-a-zA-Z0-9@:%_\+.~#?&//=]*" # urls come in `[title](url)` URL_IN_MARKDOWN_REGEX = re.compile(r"(?<=\]\()%s(?=\s*\))" % URL_REGEX) +SKIPPED_URLS = [ + "https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.69.607", # From date: 19.12.24, notebook: hamiltonian_simulation_guide.ipynb +] def test_links() -> None: @@ -34,6 +37,8 @@ def iterate_links_from_notebook(filename: str) -> Iterable[tuple[int, str]]: def _test_single_url(url: str, retry: int = 3) -> bool: + if url in SKIPPED_URLS: + return True if retry == 0: return False From f6241182a4d4042065d89631e59ce2bb94b1f2e3 Mon Sep 17 00:00:00 2001 From: Dror Segman Date: Tue, 17 Dec 2024 16:14:09 +0200 Subject: [PATCH 31/38] delete outdated note (also required to match the suggested solution) --- tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb index eedb222d9..631f157bb 100644 --- a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb +++ b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb @@ -324,10 +324,7 @@ " \\end{cases}\n", "$$\n", "\n", - "Notes:\n", - "- You cannot use `x` directly as the control variable in a `control` operator because it also occurs in the nested scope. To determine if `x` is in the lower or upper half of the domain, duplicate the most significant bit (MSB) onto a separate variable called `label`.\n", - "- In Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function (but not necessarily a Qmod function).\n", - "\n", + "Note: in Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function (but not necessarily a Qmod function).\n", "\n", "
\n", " Hint\n", From 2d497413f20051815dd896c7c289174593b4dfe7 Mon Sep 17 00:00:00 2001 From: Dror Segman Date: Tue, 17 Dec 2024 16:16:28 +0200 Subject: [PATCH 32/38] delete unnecesarry note --- tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb index 631f157bb..3700611e2 100644 --- a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb +++ b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb @@ -324,7 +324,7 @@ " \\end{cases}\n", "$$\n", "\n", - "Note: in Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function (but not necessarily a Qmod function).\n", + "Note: in Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function.\n", "\n", "
\n", " Hint\n", From e2120040e6579cf53e51e636ba40586b7624bd12 Mon Sep 17 00:00:00 2001 From: Dror Segman Date: Tue, 17 Dec 2024 16:17:35 +0200 Subject: [PATCH 33/38] delete outdated hint --- .../QMOD_workshop/QMOD_Workshop_Part_2.ipynb | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb index 3700611e2..0291cef0e 100644 --- a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb +++ b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb @@ -324,16 +324,7 @@ " \\end{cases}\n", "$$\n", "\n", - "Note: in Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function.\n", - "\n", - "
\n", - " Hint\n", - " dup_msb(x, label)
\n", - " control(label, ...) # 0.5 <= x < 1.0
\n", - " X(label)
\n", - " control(label, ...) # 0.0 <= x < 0.5
\n", - "
\n", - "\n" + "Note: in Python, assignment operators cannot be used in lambda expressions, so the computation of the function needs to be factored out to a named Python function.\n" ] }, { From 48e35ffdfe84528118281bbe9900ab7cb8ba3675 Mon Sep 17 00:00:00 2001 From: Dror Segman Date: Tue, 17 Dec 2024 16:20:12 +0200 Subject: [PATCH 34/38] fix probabilities to sum up to 1 --- tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb index 0291cef0e..835ab2561 100644 --- a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb +++ b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb @@ -409,7 +409,7 @@ "\n", "The second `bind` operation concatenates the variables back to the `res` output variable.\n", "\n", - "For this exercise, fill in the missing code parts in the above snippet and use the `control` statement to manually generate the 3-qubit probability distribution: `[1/8, 1/8, 1/8, -sqrt(3)/16, 1/8 + sqrt(3)/16, 1/8, 1/8, 1/8, 1/8]`.\n", + "For this exercise, fill in the missing code parts in the above snippet and use the `control` statement to manually generate the 3-qubit probability distribution: `[1/8, 1/8, 1/8, 1/8 - sqrt(3)/16, 1/8 + sqrt(3)/16, 1/8, 1/8, 1/8, 1/8]`.\n", "\n", "The following sequence of operations generates it:\n", "1. Perform the Hadamard transform on all three qubits.\n", @@ -440,7 +440,7 @@ " 1 / 8,\n", " 1 / 8,\n", " 1 / 8,\n", - " -sqrt(3) / 16,\n", + " 1 / 8 - sqrt(3) / 16,\n", " 1 / 8 + sqrt(3) / 16,\n", " 1 / 8,\n", " 1 / 8,\n", From 01ac7b15deb4b9bf8ff9dc29c3254d4c7f6beb58 Mon Sep 17 00:00:00 2001 From: AnnePicus Date: Thu, 19 Dec 2024 13:38:08 +0200 Subject: [PATCH 35/38] Made suggestions for the English --- algorithms/simon/simon.ipynb | 70 ++++++++++++++++++------------------ 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/algorithms/simon/simon.ipynb b/algorithms/simon/simon.ipynb index 65460c11d..0d289bd0b 100644 --- a/algorithms/simon/simon.ipynb +++ b/algorithms/simon/simon.ipynb @@ -7,7 +7,7 @@ "source": [ "# Simon's Algorithm\n", "\n", - "The Simon's algorithm [[1](#SimonsWiki)] is one of the basic quantum algorithms that demonstrates an exponential speed-up over its classical counterpart, in the oracle complexity setting. The algorithm solves the so called Simon's problem: \n", + "Simon's algorithm [[1](#SimonsWiki)] is a basic quantum algorithm that demonstrates an exponential speed-up over its classical counterpart in the oracle complexity setting. The algorithm solves the so-called Simon's problem: \n", "\n", "* **Input:** A function $f: [0,1]^N \\rightarrow [0,1]^N$.\n", "\n", @@ -23,13 +23,13 @@ "*** \n", "\n", "\n", - "Note that the condition on $f$ implies that it is 2-to-1 if $s \\neq 0^N$, and 1-to-1 otherwise. Herefter we refer to a function $f(x)$ that satisfies the condition in Eq. ([1](#mjx-eqn-1)) as a \"Simon's function\".\n", + "Note that the condition on $f$ implies that it is 2-to-1 if $s \\neq 0^N$, and 1-to-1 otherwise. Hereafter, we refer to a function $f(x)$ that satisfies the condition in Eq. ([1](#mjx-eqn-1)) as a \"Simon's function\".\n", "\n", - "Problem hardeness: The Simon's problem is hard to solve with a classical, deterministic or probabalistic, approaches. This can be understood as follows: determining $s$ requries finding a collision $f(x)=f(y)$, as $s = x\\oplus y$. What is the minimal number of calls for measuring a collision? If we take the deterministic approach, in the worst case we will need $2^{N-1}$ calls. A probablistic approach, in the spirit of the one that solves the Birthday problem [[2](#BDWiki)], has a slightly better scaling of $O(2^{N/2})$ queries.\n", + "Problem hardness: The Simon's problem is hard to solve with classical deterministic or probabalistic approaches. This can be understood as follows: determining $s$ requires finding a collision $f(x)=f(y)$, as $s = x\\oplus y$. What is the minimum number of calls for measuring a collision? If we take the deterministic approach, in the worst case we need $2^{N-1}$ calls. A probablistic approach, in the spirit of the one that solves the birthday problem [[2](#BDWiki)], has slightly better scaling of $O(2^{N/2})$ queries.\n", "\n", - "**The quantum approach requires $O(N)$ queries, thus, introducing an exponential speedup**.\n", + "**The quantum approach requires $O(N)$ queries, thus introducing an exponential speedup**.\n", "\n", - "Next, we define the Simon's algorithm, which has a [quantum part](#The-Quantum-Part) and a [classical postprocess part](#The-Classical-Postprocess). Then, we run the algorithm on two different examples of a Simon's function: one that can be defined with [simple arithmetic](#Example:-Arithmetic-Simon's-Function), and another that has a [shallow implementation](#Example:-Shallow-Simon's-Function). A [mathematical explanation](#Technical-Notes) of the algorithm is provided at the end of this notebook." + "Next, we define the Simon's algorithm, which has a [quantum part](#The-Quantum-Part) and a [classical postprocess part](#The-Classical-Postprocess). Then, we run the algorithm on two different examples of a Simon's function: one that can be defined with [simple arithmetic](#Example:-Arithmetic-Simon's-Function) and another that has a [shallow implementation](#Example:-Shallow-Simon's-Function). A [mathematical explanation](#Technical-Notes) of the algorithm is provided at the end of this notebook." ] }, { @@ -39,8 +39,8 @@ "source": [ "
\n", "\n", - "
Figure 1. The Simon's algorithm is comprised of two quantum blocks. The main part of the algorithm\n", - "is the oracle which implements the Simon's function f(x).
\n", + "
Figure 1. The Simon's algorithm comprises two quantum blocks. The main part of the algorithm\n", + "is the oracle that implements the Simon's function f(x).
\n", "
" ] }, @@ -49,7 +49,7 @@ "id": "082e2c2c-3e14-41d7-91b8-eaea042faac7", "metadata": {}, "source": [ - "## How to Build the Algorithm with Classiq" + "## Building the Algorithm with Classiq" ] }, { @@ -57,9 +57,9 @@ "id": "29bafcd3-7bf8-4c29-82ab-0df8104683c0", "metadata": {}, "source": [ - "### The Quantum Part\n", + "### Quantum Part\n", "\n", - "The quantum part of the algorithm is rather simple, calling the quantum implementation of $f(x)$, between two calls of the hadamard transform. The call of $f$ is done out-of-place, onto a quantum variable $y$, whereas only the final state of $x$ is relevant to the classical post-process to follow. " + "The quantum part of the algorithm is rather simple, calling the quantum implementation of $f(x)$, between two calls of the hadamard transform. The call of $f$ is done out-of-place, onto a quantum variable $y$, whereas only the final state of $x$ is relevant to the classical postprocess to follow. " ] }, { @@ -92,13 +92,13 @@ "id": "c9bb1577-fb51-43b8-8e81-ea8aed88dd14", "metadata": {}, "source": [ - "### The Classical Postprocess\n", + "### Classical Postprocess\n", "\n", - "The classical part of the algorithm includes the following post-processing steps:\n", - "1. Finding $N-1$ samples of $x$ that are linearly independent, $\\{y_k\\}^{n-1}_{1}$. It is gurenteed that this can be acheived with high probability, see the [technical details](#The-classical-part) below.\n", + "The classical part of the algorithm includes the following postprocessing steps:\n", + "1. Finding $N-1$ samples of $x$ that are linearly independent, $\\{y_k\\}^{n-1}_{1}$. It is guaranteed that this can be achieved with high probability (see the [technical details](#The-classical-part) below).\n", "2. Finding the string $s$ such that $s \\cdot y_k=0 \\,\\,\\, \\forall k$, where $\\cdot$ refers to a dot-product mod 2 (polynomial complexity in $N$).\n", "\n", - "For these steps we use the *Galois* package, which extends *numpy* to finite field operations." + "For these steps we use the *Galois* package, which extends *NumPy* to finite field operations." ] }, { @@ -135,7 +135,7 @@ "import galois\n", "import numpy as np\n", "\n", - "# here we work over boolean arithmetics - F(2)\n", + "# here we work over Boolean arithmetics - F(2)\n", "GF = galois.GF(2)" ] }, @@ -161,7 +161,7 @@ }, "outputs": [], "source": [ - "# The following function checks whether a set contains linearly independet vectors\n", + "# The following function checks whether a set contains linearly independent vectors\n", "\n", "\n", "def is_independent_set(vectors):\n", @@ -175,8 +175,8 @@ "\n", "def get_independent_set(samples):\n", " \"\"\"\n", - " The following function gets samples of n-sized strings from running the quantum part and return an n-1 x n matrix,\n", - " whose rows forms a set if independent\n", + " The following function gets samples of n-sized strings from running the quantum part and returns an n-1 x n matrix,\n", + " whose rows form a set if independent\n", " \"\"\"\n", " ind_v = []\n", " for v in samples:\n", @@ -193,8 +193,8 @@ "id": "2ffe2431-6dea-41fc-9125-00e5ab329ffd", "metadata": {}, "source": [ - "For the second step we simply need to solve a linear set of equations. We have $N-1$ equations on a binary vector of size $N$.\n", - "It has two solutions, one of them is the trivial solution $0^N$, and the other gives us the secret string $s$. The *Galois* package handles this task as follows:" + "For the second step we need to solve a linear set of equations. We have $N-1$ equations on a binary vector of size $N$.\n", + "It has two solutions, one of which is the trivial solution $0^N$, while the other gives us the secret string $s$. The *Galois* package handles this task as follows:" ] }, { @@ -226,7 +226,7 @@ "source": [ "---\n", "\n", - "Next we provide two different examples of Simon's function, and run the Simon's algorithm to find their secret string.\n", + "Next, we provide two different examples of Simon's function and run the Simon's algorithm to find their secret string.\n", "\n", "---" ] @@ -244,7 +244,7 @@ "id": "76a43559-06bd-42be-af16-a85d2d80b422", "metadata": {}, "source": [ - "An example of a valid $f(x)$ function that satisfies the condition in Eq. ([1](#mjx-eqn-1)) is:\n", + "An example of a valid $f(x)$ function that satisfies the condition in Eq. ([1](#mjx-eqn-1)):\n", "$$\n", "f(x) = \\min(x, x\\oplus s).\n", "$$\n", @@ -256,7 +256,7 @@ "id": "f457af6a-897d-4aa1-a3aa-86aa49530b1c", "metadata": {}, "source": [ - "### Implementing of the Simon's Function" + "### Implementing the Simon's Function" ] }, { @@ -347,7 +347,7 @@ "id": "0e8c6ff9-580c-4577-9e83-fe3c60761b08", "metadata": {}, "source": [ - "By plotting the results we can see that this is two-to-one function:" + "By plotting the results we can see that this is a two-to-one function:" ] }, { @@ -398,7 +398,7 @@ "source": [ "### Running the Simon's Algorithm\n", "\n", - "Taking $N$ number of shots gurentees getting a set of $N-1$ independet strings with high probability (assuming a noiseless quantum computer), see [technical explanation](#The-quantum-part) below. Moreover, increasing the number of shots by a constant factor provides an exponential improvment. Below we take $50*N$ shots." + "Taking $N$ number of shots guarantees getting a set of $N-1$ independent strings with high probability (assuming a noiseless quantum computer) (see [technical explanation](#The-quantum-part) below). Moreover, increasing the number of shots by a constant factor provides an exponential improvment. Here we take $50*N$ shots." ] }, { @@ -437,7 +437,7 @@ "id": "fe7d80e1-875a-494e-8fa2-ffead27178fa", "metadata": {}, "source": [ - "We Synthesize and execute to obtain the results " + "We synthesize and execute to obtain the results: " ] }, { @@ -529,7 +529,7 @@ "$$\n", "The function $f$ operates as follows: for the first $N-L$ elements we simply \"copy\" the data, whereas for the last $L$ elements we apply a xor with the $N-L$ element. A simple proof that this is indeed a 2-to-1 function is given in Ref. [[3](#SimonsPaper2024)].\n", "\n", - "*Comment:* Ref. [[3](#SimonsPaper2024)] employed further reduction of the function implementation (reducing the $N$-sized Simon's problem to an $(N-L)$-sized problem), added a classical post-process of randomly permutating over the result of $f(x)$ to increase the hardness of the problem, as well as included some NISQ analysis. These steps where taken to show an algorithmic speedup on real quantum hardware." + "*Comment:* Ref. [[3](#SimonsPaper2024)] employed further reduction of the function implementation (reducing the $N$-sized Simon's problem to an $(N-L)$-sized problem), added a classical postprocess of randomly permutating over the result of $f(x)$ to increase the hardness of the problem, and also included some NISQ analysis. These steps were taken to show an algorithmic speedup on real quantum hardware." ] }, { @@ -537,7 +537,7 @@ "id": "65a1970a-ec4e-40ca-89f1-cb341d70b08a", "metadata": {}, "source": [ - "### Implementing of the Simon's Function\n", + "### Implementing the Simon's Function\n", "\n", "The first $N-L$ \"classical copies\", $|x_k,0\\rangle\\rightarrow |x_k x_k\\rangle$, can be implemented by $CX$ gates. The xor operations, $|x_k,0\\rangle\\rightarrow |x_k, x_k \\oplus x_{N-L}\\rangle$, can be implemented by two CX operations, one for a \"classical copy\" of $x_k$, followed by a $CX$ operation to apply a xor with $x_{N-L}$." ] @@ -580,7 +580,7 @@ "id": "82f3f088-cb2c-4539-a29a-37e8d3d894b0", "metadata": {}, "source": [ - "Below we take a specific example, and plot $f(x)$ for all possible $x$ values: " + "Here we take a specific example and plot $f(x)$ for all possible $x$ values: " ] }, { @@ -692,7 +692,7 @@ "id": "4297c903-f4f8-436b-a62f-755b037012b9", "metadata": {}, "source": [ - "We synthesize and execute to obtain the results " + "We synthesize and execute to obtain the results: " ] }, { @@ -782,7 +782,7 @@ "source": [ "## Technical Notes\n", "\n", - "Below we provide some technical details about the quantum and classical parts of the Simon's algorithm" + "This section provides some technical details about the quantum and classical parts of the Simon's algorithm." ] }, { @@ -790,7 +790,7 @@ "id": "1a0b7c46-89db-4f0e-8dac-df1529525ec6", "metadata": {}, "source": [ - "### The Quantum Part\n", + "### Quantum Part\n", "\n", "Following the three blocks of the algorithm:\n", "$$\n", @@ -816,9 +816,9 @@ "id": "fed2c380-c13c-4f17-b6e3-4a41d7eeadc0", "metadata": {}, "source": [ - "### The Classical Part\n", + "### Classical Part\n", "\n", - "We have a set of possible $y$ values that can be measured, each with the same probability of $1/M$, where $M$ is the set size. In the case that $s=0^N$ we have $M=2^N$, whereas for $s\\neq 0^N$ the set size is $M=2^{N-1}$. The probability to measure a set of $N-1$ linearly independent binary strings $y$ can be calculated as follows (see also the Birthday problem [[2](#BDWiki)]): For the first string we just require that we do not pick $y=0^N$, so $P(y_0)=1-1/M$. Then, for the next string, we require that it is not in $\\left\\{a_0 y_0\\,\\,\\,| a_0=0,1\\right\\}$, thus $P(y_1)=(1-2/M)$. The following string is required not to be picked out of $\\left\\{a_0 y_0+a_1y_1\\,\\,\\,| a_0, a_1=0,1\\right\\}$. We can continue with this procedure up to $y_{N-1}$ to get\n", + "We have a set of possible $y$ values that can be measured, each with the same probability of $1/M$, where $M$ is the set size. If $s=0^N$, we have $M=2^N$, whereas for $s\\neq 0^N$ the set size is $M=2^{N-1}$. The probability to measure a set of $N-1$ linearly independent binary strings $y$ can be calculated as follows (see also the birthday problem [[2](#BDWiki)]): For the first string we just require that we do not pick $y=0^N$, so $P(y_0)=1-1/M$. Then, for the next string, we require that it is not in $\\left\\{a_0 y_0\\,\\,\\,| a_0=0,1\\right\\}$, thus $P(y_1)=(1-2/M)$. The following string is required not to be picked out of $\\left\\{a_0 y_0+a_1y_1\\,\\,\\,| a_0, a_1=0,1\\right\\}$. We can continue with this procedure up to $y_{N-1}$ to get\n", "$$\n", "P_{\\rm independent} = \n", "\\left\\{\\begin{array}{l l}\n", @@ -843,7 +843,7 @@ "\n", "[2]: [Birthday problem (Wikipedia)](https://en.wikipedia.org/wiki/Birthday_problem)\n", "\n", - "[3]: [Singkanipa P., et al. \"Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem.\" arXiv preprint arXiv:2401.07934 (2024).](https://arxiv.org/abs/2401.07934)" + "[3]: [Singkanipa P. et al. \"Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem.\" arXiv preprint arXiv:2401.07934 (2024).](https://arxiv.org/abs/2401.07934)" ] } ], From ede2fc21d4d2601831f217c559bf833871de29f9 Mon Sep 17 00:00:00 2001 From: Nadav Ben Ami Date: Sun, 22 Dec 2024 11:13:58 +0200 Subject: [PATCH 36/38] updating notebook and tests requirements --- algorithms/simon/simon.ipynb | 213 +++--------------- .../simon_example.synthesis_options.json | 38 ++++ ...mon_shallow_example.synthesis_options.json | 44 +++- requirements_tests.txt | 1 - 4 files changed, 116 insertions(+), 180 deletions(-) diff --git a/algorithms/simon/simon.ipynb b/algorithms/simon/simon.ipynb index 0d289bd0b..342b2d02b 100644 --- a/algorithms/simon/simon.ipynb +++ b/algorithms/simon/simon.ipynb @@ -66,14 +66,7 @@ "cell_type": "code", "execution_count": 1, "id": "e4307bea-48a6-4561-9325-dcc60f3e5e52", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:04.513790Z", - "iopub.status.busy": "2024-05-07T14:48:04.513297Z", - "iopub.status.idle": "2024-05-07T14:48:07.284978Z", - "shell.execute_reply": "2024-05-07T14:48:07.284234Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from classiq import *\n", @@ -105,14 +98,7 @@ "cell_type": "code", "execution_count": 2, "id": "ec2fe11a-5ecf-44cf-9f2c-b8530bf1ad3a", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:07.290106Z", - "iopub.status.busy": "2024-05-07T14:48:07.288609Z", - "iopub.status.idle": "2024-05-07T14:48:07.293547Z", - "shell.execute_reply": "2024-05-07T14:48:07.292945Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# !pip install galois" @@ -122,14 +108,7 @@ "cell_type": "code", "execution_count": 3, "id": "2f3f89eb-dca1-410d-a4d1-441bd89cd11b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:07.297573Z", - "iopub.status.busy": "2024-05-07T14:48:07.296517Z", - "iopub.status.idle": "2024-05-07T14:48:10.434951Z", - "shell.execute_reply": "2024-05-07T14:48:10.434360Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import galois\n", @@ -151,14 +130,7 @@ "cell_type": "code", "execution_count": 4, "id": "53bb09e7-3907-4ec6-b4d5-e4c6f34e9d8d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:10.439194Z", - "iopub.status.busy": "2024-05-07T14:48:10.438242Z", - "iopub.status.idle": "2024-05-07T14:48:10.444058Z", - "shell.execute_reply": "2024-05-07T14:48:10.443506Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# The following function checks whether a set contains linearly independent vectors\n", @@ -201,14 +173,7 @@ "cell_type": "code", "execution_count": 5, "id": "e01d7967-0520-45d1-adda-57d7f300e0c0", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:10.446364Z", - "iopub.status.busy": "2024-05-07T14:48:10.446186Z", - "iopub.status.idle": "2024-05-07T14:48:10.449640Z", - "shell.execute_reply": "2024-05-07T14:48:10.449027Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def get_secret_integer(matrix):\n", @@ -271,14 +236,7 @@ "cell_type": "code", "execution_count": 6, "id": "4a353a36-5da0-4a2d-a44a-1c08669ee616", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:10.451942Z", - "iopub.status.busy": "2024-05-07T14:48:10.451768Z", - "iopub.status.idle": "2024-05-07T14:48:10.455133Z", - "shell.execute_reply": "2024-05-07T14:48:10.454528Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from classiq.qmod.symbolic import min\n", @@ -301,20 +259,13 @@ "cell_type": "code", "execution_count": 7, "id": "8ebfa471-7675-4a97-9655-8475495ac55b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:10.457422Z", - "iopub.status.busy": "2024-05-07T14:48:10.457052Z", - "iopub.status.idle": "2024-05-07T14:48:17.406429Z", - "shell.execute_reply": "2024-05-07T14:48:17.405785Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://platform.classiq.io/circuit/61cffecc-7617-4bb7-b554-4ec59239f1ae?version=0.41.0.dev39%2B79c8fd0855\n" + "Opening: https://platform.classiq.io/circuit/9a4e6522-32f2-4d6e-8f3d-f17eab1c805f?version=0.62.0\n" ] } ], @@ -331,7 +282,7 @@ "\n", "\n", "qmod = create_model(main)\n", - "qmod = update_constraints(qmod, optimization_parameter=\"width\")\n", + "qmod = update_constraints(qmod, optimization_parameter=OptimizationParameter.WIDTH)\n", "\n", "# synthesize\n", "qprog = synthesize(qmod)\n", @@ -354,18 +305,11 @@ "cell_type": "code", "execution_count": 8, "id": "2f0662ee-ed60-4504-ba2c-30ba1bca8e3e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:17.409478Z", - "iopub.status.busy": "2024-05-07T14:48:17.408936Z", - "iopub.status.idle": "2024-05-07T14:48:17.951846Z", - "shell.execute_reply": "2024-05-07T14:48:17.950842Z" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -405,14 +349,7 @@ "cell_type": "code", "execution_count": 9, "id": "ee24ec02-1eef-4ae1-9905-d9722570146e", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:17.961126Z", - "iopub.status.busy": "2024-05-07T14:48:17.959565Z", - "iopub.status.idle": "2024-05-07T14:48:17.992767Z", - "shell.execute_reply": "2024-05-07T14:48:17.991839Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from classiq.execution import ExecutionPreferences\n", @@ -426,7 +363,7 @@ "\n", "qmod = create_model(\n", " main,\n", - " constraints=Constraints(optimization_parameter=\"width\"),\n", + " constraints=Constraints(optimization_parameter=OptimizationParameter.WIDTH),\n", " execution_preferences=ExecutionPreferences(num_shots=50 * NUM_QUBITS),\n", " out_file=\"simon_example\",\n", ")" @@ -442,16 +379,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "1223b440-0939-444f-8042-bcae991a443d", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:18.023200Z", - "iopub.status.busy": "2024-05-07T14:48:18.022015Z", - "iopub.status.idle": "2024-05-07T14:48:24.755464Z", - "shell.execute_reply": "2024-05-07T14:48:24.754716Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "qprog = synthesize(qmod)\n", @@ -461,16 +391,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "8d1bf53a-7906-4ab6-b40b-061ac38a560b", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:24.758795Z", - "iopub.status.busy": "2024-05-07T14:48:24.758243Z", - "iopub.status.idle": "2024-05-07T14:48:24.852862Z", - "shell.execute_reply": "2024-05-07T14:48:24.852185Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "matrix_of_ind_v = get_independent_set(samples)\n", @@ -482,16 +405,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "3af79a3a-a37b-416c-9580-02392b45305f", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:24.855688Z", - "iopub.status.busy": "2024-05-07T14:48:24.855370Z", - "iopub.status.idle": "2024-05-07T14:48:24.859074Z", - "shell.execute_reply": "2024-05-07T14:48:24.858437Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -544,16 +460,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "718afafc-a0ed-49c3-a793-e8cba7eac6c1", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:24.861699Z", - "iopub.status.busy": "2024-05-07T14:48:24.861256Z", - "iopub.status.idle": "2024-05-07T14:48:24.865718Z", - "shell.execute_reply": "2024-05-07T14:48:24.865158Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "@qfunc\n", @@ -587,25 +496,18 @@ "cell_type": "code", "execution_count": 15, "id": "7024f574-2ea3-4864-aa14-8155466a11a4", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:24.868151Z", - "iopub.status.busy": "2024-05-07T14:48:24.867723Z", - "iopub.status.idle": "2024-05-07T14:48:28.922759Z", - "shell.execute_reply": "2024-05-07T14:48:28.921998Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Opening: https://platform.classiq.io/circuit/c9184a7a-af13-4a42-9fa9-c7457ce35f89?version=0.41.0.dev39%2B79c8fd0855\n" + "Opening: https://platform.classiq.io/circuit/58485345-c1a8-4651-986b-6e4b2b3249fd?version=0.62.0\n" ] }, { "data": { - "image/png": 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45513kJ6ebt3266+/Wv9/wIABDn9XdHQ0AKCysrLDNo1GI4zGv74EDQYDAJYE6U6cFG12JW6niKdOTja14L2sEfD20uF4gxHhgb5IHBgCby+dXVtyPEap22Su8oqTok055yrmHFDX1IJVWZfAq4NzgCdylTpOijZZEqSNF198Ed7e3hg/fjwGDBiAP//8E3v27MGiRYuwfft2+Pj4oKSkBGPHjgUAbN++HcnJyQDOdkavXvbjvw0bNiAtLQ29e/e2Gfi0t2jRIuTm5tptZ0kQ9dldo8Oqg/ZPN7Z3+4UmjAiT/ceGiLqI5wB100RJEEEQMGXKFBQWFmL48OH4/vvvAbh2YOToilF0dDRLgig8V1OrgNLDJ23+xVd6+CSmv1Pa6e9cfWcikjqYYCmXY5Rjm8xVXnFaz7X9ecDUKuCOd3d3+vs6Owe4I1e5xSkxV02UBNHpdMjNzUVhYSH27NmDqqoqREdHIzAw0LpPY2Mj9Hq9XeypU6cAoMPOAQBfX1+7OUwAS4L0JE6KNtvG2T5xclaU3g9PToxHlN4PR+ubO3zqZHRsuKgJlvx7uD5OijaZq7ziXNWmo/OA2CfPxJ4DXJWrnOOkaJMlQTpx0UUXWf//yJEjAICBAwdat/32228O46qqqgAAgwYNcl9yJDvOnjg5Wt+M2fllmDz87ORJPnVCpF588ow6oviB0YkTJ6z/b7lSFBQUZF3RurTU8a0Ry/aEhAQ3Z0hyIabW0Wd7qpGXeRki9baLtEXq/RwWiCQiZRH79GlEEM8BWqXoW2kA8OGHHwI4OxiKi4uzbp8yZQqef/555OfnY8aMGTYxp06dQlFREQDg+uuv91yyJCmxtY5C+vhi22NXY8fPx1GydSfSUpK6dOmciORLzHmgrqkFa2YmQBBMPAdokOyvGH3//ff47LPPcOaM7WrCra2tWLFiBR5//HEAwIMPPmhzz3HOnDkICAjAxo0b8dZbb1m3m0wm3Hfffairq8PIkSORlpbmmQMhyXWl1pG3lw5JMaEYESYgKSaUJ0QilRB7HqhpNPIcoFGyv2L066+/YsqUKQgJCUFCQgIiIiJQV1eHvXv3WucP3XLLLVi4cKFN3Lnnnot3330Xt9xyC2bNmoUVK1Zg0KBB+O677/DLL78gIiIC+fn5rJOmYqx1RESse0hdJfuB0fDhwzFnzhyUlpZi//79+M9//gNBEBAREYEbbrgBM2bMQEZGhsPYG2+8Eeeffz6WLFmCrVu3ory8HFFRUZg9ezaefPJJREREePhoyFNY64iIWPeQukP2A6OYmBi8+OKL3Y4fMWIE1q1b58KMSO4sT5y0P+kdMxit23SAzet84oRIXZyfB5pFnwdaTW5Pk2RI9nOMiLqCT5wQkZjzQEiADyKCbG+r8TxAgAKuGMkVa6XJM1fWOuoa5ur6OCnaZK62pK572JNYpcRJ0SZrpclMXl4e8vLyYDKZUFFRwVppMsVaR0TE8wA5oolaaVIwGAzQ6/WslSbTXHdW1rqk3pkncpU6jrkyV7Xm6qrzgBb6VQvHaInVRK00KbFWmjxzHR0b7tJ6Z+7MVS5xUrTJXOUVJ0Wb7szV1ecBLfSrFo5RLE6+JlXx9tJh4aR4AKx1RKRVPA9QT3BgRKqTPjQKy6cnsN4ZkYbxPEDdxVtppErpQ6NwTXwk650RaRjPA9QdHBiRalnqnZ34ibWOiLSK5wHqKt5KIyIiIjLjwIiIiIjIjAMjIiIiIjPOMeomlgRhrkqPk6JN5iqvOCnaZK7qiJOiTZYEkRmWBCEiIlIulgRxE5YEYa5qiWOuzJW5MlctHKMlliVB3IwlQZirWuKkaJO5yitOijaZqzripGiTJUGIiIiIPIQDIyIiIiIzDoyIiIiIzDgwIiIiIjLjwIiIiIjIjAMjIiIiIjMOjIiIiIjMuI5RN7EkCHNVepwUbTJXecVJ0SZzVUecFG2yJIjMsCQIERGRcrEkiJuwJAhzVUtcR7GmVgGlh0/ieIMR4YG+SBwYAm8vnSxzlVuc1nN113vHHbm6K05JuWrhGC2xLAniZiwJwlzVEtc+tnhvNXKL9qG6vtn6epTeDwsnxSN9aJSscpVznBRtSp2rJ947rsrVE3FStMlj7DlOviYiq+K91bh3dZnNFxsAHK1vxr2ry1C8t1qizEju+N4hteDAiIgAnL0Fklu0D47urVu25Rbtg6mVd9/JFt87pCYcGBERAGBXZa3dv/bbEgBU1zdjV2Wt55IiReB7h9SEAyMiAgAcb3D+xdad/Ug7+N4hNeHkayKNMrUK2FlZi901OvSrrEVYH19RceGBfm7OjOSO7x1SMw6MiDTI9ukhb6w6WIrIID8EB/igvqnF4VwRHYBIvR9GxYSi1XTGwxmTXPC9Q2rHgRGRxlieHmr/BXbM0GzdpgNsXresQrNwUjy8vXRoNbk9TZIhvndICzjHiEhDOnt6SAcgJMAHEUG2t0Yi9X5YPj3Bbi0a0g6+d0greMWom1grjbkqMW6niKeHTja14L2sEfD20tmtXty+Lf495NGmJ3KV+r3Tk1g596tS46Rok7XSZIa10kiJWgXgkEEHQwsQ5APUnwbe/9m707jbLzRhRBhPDVrG9w6pDWuluQlrpTFXpcT9+7/H8NQX+3HUYLRuCwnwwcmmzv/ltPrORCTFhHosV3fGMteux8rxvdOTWLn0q5rilJgra6W5GWulMVc5xxXvrcYDH+6xmw/S2Reb5emh0bHhNoU/3Zmrp2KZq7hYub93ehLL94Dr46Rok7XSiKhLOpok21b7r672Tw+R9vC9Q6TggdGjjz4KnU4HnU6Hp556yul+GzduREZGBsLCwuDv748hQ4ZgwYIFOHXqlAezJfKczsozWIT06W3zM58eIr53iBR6K2379u1YtmwZdDodOpoi9eKLLyI7Oxs6nQ4pKSmIiIjA1q1bsWTJEqxbtw7btm1DWFiYBzMncj+xZReenHgRzunrg5KtO5GWkiT6FgipF987RAq8YtTU1ISsrCxERUXh73//u9P9ysvLMW/ePHh7e2P9+vXYvHkzPvroIxw6dAjjx4/HgQMHcM8993gwcyLXa1uaYWdlLUytguiyC5F6fyTFhGJEmICkmFB+sWlQ+/eP2NIefO+QminuilFOTg4OHjyI9evX46OPPnK639KlSyEIAmbMmIEJEyZYtwcEBGDFihU4//zzsW7dOuzfvx9DhgzxROpELuWoNEOU3g9PToxHlN4PR+ubWZ6BnGJpDyLHFHXF6JtvvsGrr76K22+/HRkZGU73O336NNavXw8AyMzMtHt94MCBSE5OBgAUFBS4J1kiN7KUZmg/H+RofTNm55dh8vCzcz04SZYccfb+OWZoRp15UMT3DmmVYgZGp06dwp133omIiAi89NJLHe5bUVGBpqYmAEBiYqLDfSzby8vLXZonkbt1VpoBAD7bU428zMsQqbe9rcZJssTSHkQdU8yttIcffhiVlZUoKChASEhIh/tWVlYCAIKDgxEYGOhwn+joaJt9nTEajTAa/1rkzGAwAGBJkO7ESdGmGnMVU5qhur4ZQX7e+Do7BaWHT8qmtIca/x5yaJNlYeQVJ0WbPEZxsWIoYuXrkpISXHvttbj55pvxwQcfWLdnZWXhvffew+LFi/HEE09Yt+fn5+PWW2/FeeedhyNHjjj8nW+99RZmzZqFwYMH48CBA07bXrRoEXJzc+22syQISWV3jQ6rDrI0A3UP3z+kVWJLgsj+ilF9fT1mzpyJc845B6+++qrH28/JyUF2drb1Z4PBgOjoaKSlpbEkCHOVJK5fZS1WHSzt9PelpSR1WJrBE7m6Ko65ui7OVe8f9qu82uQxdh5bWFgoal/ZD4zmzJmDI0eOYO3ataLXHLLcPmtsbHS6j2WBx45GjQDg6+sLX1/7R1hZEoS5ShU3OjZc1FNnXVlbRm7HKKc21Zarq98/7Fd5tclj7DnZT74uKChAr1698M9//hNjx461+a+4uBgAsGLFCowdOxY333wzAGDQoEEAgLq6OjQ0NDj8vVVVVTb7EimFt5cOCyfFA+CTQ9R1fP8QdUz2V4wA4MyZM9i8ebPT13/99Vf8+uuvGDhwIAAgLi4OAQEBaGpqQmlpKcaNG2cXU1p69lJyQkKCe5ImcqP0oVFYPj2hzTo0Z0Xq/bBwUjyfHKIO8f1D5JzsB0Z1dXVOX3M2+bp3796YOHEiPv74Y+Tn59sNjA4fPozt27cDAKZMmeKWvIncLX1oFK6Jj8SOn4+zNAN1Gd8/RI7J/lZad82fPx86nQ4rV6603nIDzs5KnzlzJkwmE6ZOncpVr0nRvL10LM1A3cb3D5E91Q6MEhISsGzZMphMJmRkZGDcuHGYNm0aYmNjsWnTJsTFxeH111+XOk0iIiKSEdUOjABg7ty52LBhA6699lr88MMPKCwsRN++fZGTk4PvvvtO9FNuREREpA2yn2PUkXfffRfvvvtuh/ukpqYiNTXVMwkRERGRoqn6ihERERFRVyj6ipGUWCuNuSo9Too2mau84qRok7mqI06KNlkrTWby8vKQl5cHk8mEiooK1kojIiJSELG10jgw6iKDwQC9Xo+amhrWSmOuio5jrsyVuTJXLRyjJbawsFAdRWTlirXSmKta4qRok7nKK06KNpmrOuKkaFPztdKIiIiIPIUDIyIiIiIzDoyIiIiIzDgwIiIiIjLjwIiIiIjIjAMjIiIiIjMOjIiIiIjMuI5RN7EkCHNVepwUbTJXecVJ0SZzVUecFG2yJIjMsCQIERGRcrEkiJuwJAhzVUuc1nM1tQooPXwSxxuMCA/0ReLAEHh76dyaq7valFO/Mldl5aqFY7TEsiSIm7EkCHNVS5wUbUqda/HeauQW7UN1fbP19Si9HxZOikf60Ci35OqJNqXuV0/ESdGmFnLVwjGKxcnXRKQpxXurce/qMpsBCgAcrW/GvavLULy3WhVtElH3cGBERJphahWQW7QPjuYPWLblFu2DqdV1MwykaJOIuo8DIyLSjF2VtXZXbdoSAFTXN2NXZa2i2ySi7uMcIyJSLVOrgJ2Vtdhdo0O/ylr875S4R3aPNzQDcD45U25tEpHrcGBERKpkO9nZG6sOliK0T29RseGBfoppk4hciwMjIlIdy2Tn9rN2Tjae7jBOByBS74dRMaFoNZ2RfZtE5HqcY0REqiJmsrMjltWEFk6Kt1lbSK5tEpF78IpRN7EkCHNVepwUbXoi152dTHa2CAnwwcmmv35XpN4XCyYMwfi4MJvPt5hcpWizLb4H5BUnRZs8RnGxYnDla5FYEoRIGXbX6LDqoHen+90Wa4K+N2BoAYJ8gAuCBHT3oo0UbRJR17AkiJuwJAhzVUucWnJtX2bD1Crgjnd3d/r7Vt+ZiKSY0G7lKkWbHdH6e0BucUrKVQvHaIllSRA3Y0kQ5qqWOCnadGeZjcggXwQH+KC+qcXh/B7LZOfRseGi5vW0z1WKNsXS4ntAznFStMlj7DkOjIhIkZw9BXbMYLRu08F28nNPJztL0SYReRafSiMixensKTAdgOAAH0QE2a4NFKn3w/LpCXZFW+XaJhF5Hq8YEZHiiCmzUdfUgjUzEyAIJpRs3Ym0lCTRt7Lk0iYReR6vGBGR4pwtn9G5mkYjkmJCMSJMQFJMaI8GKFK0SUSex4EREcle2/pjOytrEdbXV1Rcd8tstG/P1CqI/l0s7UGkbLyVRkSy5qj+mNinwLpb2qN9e1F6Pzw58SJE6f1wtL7Z5W0SkXzwihERyZblKbD2c3uOGYyoMw+K2t+o6slTYM7aO1rfjNn55Zg8PMqmDVe0SUTy0uMrRseOHcOmTZtQVlaGY8eO4eTJkwgJCUFERARGjBiBq6++GhEREa7IlYg0RMxTYPoAH/j18sZRQ5s1hfR+WDgpvstPgYlp77M91cjLTMDi9e3WMepmm0QkP90aGLW0tGDt2rXIy8vDrl27AACOFtDW6c7+yykpKQmzZ8/GTTfd5NZFmTyJtdKYq9LjpGjTlfXHLE+Brcq6BF5eOusq1IkDQ+DtpbNrq6c11gQA1fXNCPLzwtfZKTYrX3e3zfb4HpBXm1rIVQvH2NWYLpcEef/995GTk4Pq6moIgoBzzjkHo0ePxsUXX4x+/fohKCgI9fX1OHHiBPbu3YsdO3bgxIkT0Ol0OPfcc7F06VJMnz69ywclNdZKI/IssfXHbr/QhBFhPa9s5On2iMiz3FIrbfTo0di1axfCwsKQmZmJrKwsDB8+vNO477//HitXrsQHH3yAEydOICkpCdu3bxfbrKywVhpzVUuc3HPdWVmL6e+Udvo7XVV/zFXtdaVNqeOYK3PVwjFaYt1SK+3gwYN47rnncP/998PXV9zjsgBw6aWX4uWXX8Zzzz2HV155Bc8++2xXmpUl1kpjrmqJk6JNMXGjY8NFPQXmqvpjrm5PTJtyiZOiTeaqjjgp2nR3rbQuPZX2yy+/YN68eV0aFLXl6+uLRx55BL/88ku34olIO7y9dFg4KR6AZ54C83R7RCRPXRoYtb/0VF9f361GO7qE5ciaNWtw++23Y/jw4QgPD4ePjw/0ej1GjRqFpUuX4tSpU05jN27ciIyMDISFhcHf3x9DhgzBggULOowhInlIHxqF5dMTEKn3TP0xT7dHRPLTo8f1x40bhw0bNqBfv36uyseh5cuXY/v27bjooouQkJCA0NBQHDt2DDt27MB3332Hd955B5s3b8a5555rE/fiiy8iOzsbOp0OKSkpiIiIwNatW7FkyRKsW7cO27ZtQ1hYmFtzJ6KeSR8ahWviI7Hj5+MeqT/m6faISF56NDD6/vvvceWVV2LTpk2IjIzscN+WlpZu3xNctmwZLrzwQoSG2k54PHHiBK677jps27YN8+bNwwcffGB9rby8HPPmzYO3tzeKioowYcIEAGdnpU+ePBmbNm3CPffcg08++aRbORGR53h76ZAUE4oTP3mm/pin2yMi+ejRytePPvoofvrpJ6SkpOC3335zut/atWsxZMiQbreTlJRkNygCgH79+mHJkiUAgJKSEpvXli5dCkEQMGPGDOugCAACAgKwYsUKeHl5Yd26ddi/f3+38yIiIiJ16dHA6JlnnsHTTz+NQ4cOISUlBQcPHrR5/dtvv8UVV1yBzMxM/Prrrz1pyqlevc5e9Go7Ifz06dNYv349ACAzM9MuZuDAgUhOTgYAFBQUuCUvIiIiUp4e10rLyclBXl4ejhw5giuvvBI//vgjfv31V0ybNg3Jycn49ttvMWDAALz33nuuyNdGQ0MDFi1aBACYPHmydXtFRQWampoAAImJiQ5jLdvLy8tdnhcREREpU49rpQHAvffei6CgIMyYMQMpKSkwGo0wGo0IDQ3F448/jvvvvx+9e/fucTslJSXIz89Ha2urdfJ1Q0MD0tPTbdZGqqysBAAEBwcjMDDQ4e+Kjo622dcZy7FYGAwGACwJ0p04KdrUQq5aOEYp2mSu8oqTok0t5KqFY+xqTJdLgjjS2tqKt99+Gzk5OTh58iR0Oh2mTZuG5cuXQ6/X9/TXW7300kuYO3euzbbMzEy88MILNoVq8/Pzceutt+K8887DkSNHHP6ut956C7NmzcLgwYNx4MABp20uWrQIubm5dttZEoSIiEg5xJYE6fEVo4KCAixYsAAHDhyAIAi44oorsGPHDmzcuBGVlZW49NJLe9qE1Zw5czBnzhy0tLTgt99+Q2FhIZ566ikUFxejoKAAV155pcvassjJyUF2drb1Z4PBgOjoaKSlpbEkCHNVdBxzZa7Mlblq4RgtsYWFhaL27dHA6IorrsDOnTshCAISEhKwbNkyXHXVVVi5ciVmzZqFq6++GkVFRdaJzq7i4+ODCy64ANnZ2UhOTsbo0aMxffp0HDhwAP7+/tbbZ42NjU5/h2WBx84Wm/T19XW40jdLgjBXtcRJ0SZzlVecFG0yV3XESdGmrEqCtPftt9/ivPPOw3vvvYfS0lJcddVVAIAZM2YgPz8fjY2NuPbaa7FhwwaXJOtIUlIS4uPjUVVVhdLSswUgBw0aBACoq6tDQ0ODw7iqqiqbfYmIiIh6NDBavHgxKioqcNttt9m9duONN6KgoACtra2YPHmyWx+L79OnDwDg+PHjAIC4uDjr/B/LYKk9y/aEhAS35UVERETK0qOB0YIFC+Dn5+f09YyMDHz55Zfw8fHBzTff3JOmnKqpqcGePXsAAIMHDwYA9O7dGxMnTgRwdpJ0e4cPH8b27dsBAFOmTHFLXkRERKQ8PV7HqDNXXXUVNm7c6PSx+c7s27cPa9asQXNzs91rFRUVuPHGG2E0GnH55Zdj2LBh1tfmz58PnU6HlStXori42Lq9qakJM2fOhMlkwtSpU3u0IjcRERGpi0vWMerMqFGj8M0333Qr9vjx45g+fTruvvtuXHbZZejfvz9Onz6N3377DWVlZWhtbcVFF12EtWvX2sRZJoNnZ2cjIyMDV111FcLDw7F161ZUV1cjLi4Or7/+uguOjoiIiNTCIwMjABg6dGi34i6++GI8/fTT2Lp1K/bv34/y8nK0tLQgNDQU48ePx/XXX48ZM2Y4fHJs7ty5GDZsGJYtW4Zdu3ahsbERAwYMQE5ODnJycrp9FYuIiIjUqUsDo4ceegj/7//9P/Tr16/bDf7vf//D4sWL8corr4ja/5xzzsHjjz/e7fZSU1ORmpra7XgiIiLSji4NjF577TWsXLkSs2fPxp133okLL7xQdOyBAwfw9ttv44033sCff/4pemAkVywJwlyVHidFm8xVXnFStMlc1REnRZuyLAny/fff4/7778f27duh0+kwevRojB8/HqNHj8ZFF12Efv36oW/fvjh16hROnDiBffv2YceOHdiwYQN27doFQRCQnJyMV1991aUrYntCXl4e8vLyYDKZUFFRwZIgRERECiK2JEiXBkaPPfYYJk+ejOrqarz44ovYsWPH2V+i0zmNsfz6K664AnPnzsXUqVPFNidLBoMBer0eNTU1LAnCXBUdJ7dcTa0CSg+fxPEGI8IDfZE4MATeXjpRsZ7OVW5tOovrrE/llKsc29RCrlo4RktsYWGh62ulPf/886ipqcGKFStwww03oLS0FJ9//jm++uorlJeX25Tg6NOnDxISEjBu3Dhcd911irtC1BmWBGGuaomTos32ccV7q5FbtA/V9X8tyxGl98PCSfFIHxolq1zl3GbbuK70qdS5yr1NLeSqhWMUq0sDI29vb5hMJuvPSUlJuO+++7BlyxYAZy9T1dfXIzg4GP7+/q7NlIhUqXhvNe5dXYb2l66P1jfj3tVlWD49weEXOTnHPiXqvi4t8BgaGoojR45YfxYEweYqUUBAAKKiojgoIiJRTK0Ccov22X2BA7Buyy3aB1Or6Dv+msc+JeqZLg2MEhISsHnzZrzxxhswGo3uyomINGJXZa3NrZ72BADV9c3YVVnruaQUjn1K1DNdGhg9+uijAID77rsPISEh0Ol02L17N95++22UlZV16xE6ItKu4w3Ov8C7sx+xT4l6qksDo3HjxuGrr77C+PHjrXONfvzxR9x9990YOXIkAgMDkZiYiLvvvhtvvvkmdu/ezcESEQE4e4tnZ2UtdtfosLOyFqZWAeGBzotQtyV2Py1q369hfe2rADjCPiVyrMslQVJSUlBSUoI///wTffr0wahRo5CYmIjS0lL88MMPKCsrQ1lZGd5++20AZ2ePX3zxxRgxYgTefPNNlx8AEcmf7RNS3lh1sBRRej88OTEeUXo/HK1vdjgnRgcgUu+HUTGhaDWd8XDW8ueoXyODfBEc4IP6ppZO+5SI7HW7VpplgnV8fDxee+01AIDJZMJ///tf7N69G7t377YOlsrLy/H9999zYESkQR09ITU7vwyzrozBm1sqoQNs9rGstrNwUjy8vXRoNYHacNavxwxG67bO+pSI7PWoiOyhQ4fQ1NRk/dnb2xuXXHIJLrnkEsyYMQOA7WCJiLSlsyekdAA+21ONvMzLsHj9TzaThiM7WHNH68T0qz7AB369vHHUwD4l6ooeDYxiYmI63aftYElNWCuNuSo9zhNt7hT5hFSQnze+zk5xuEpz+7bYr+L6ta6pBauyLoGXl85pn3oiV1fFSdGmFnLVwjF2NaZLJUG0jLXSiLpud40Oqw56d7rf7ReaMCKMpyKx2K9EXeeWWmnEWmnMVT1x7mizfW0uU6uAO97t/Db66jsTkdTJZGD261/92toq4Hb2q2zjlJSrFo7REuuWWmn0F9ZKY65qiXNVm45qc4l9Qmp0bLjoycDsVyAyyI/9ylxlESdFm7KqlUZE5AifkHIP5/3azH4lcpMuLfBIRNSemCekggN8EBFku6BgpN6PxUw7IKZfQwJ8EBFku6Aj+5WoZ3jFiIh6RExtrrqmFqyZmQBBMKFk606kpSR16TaPFonp15NNLVhzVxKEVvYrkatwYEREXdK2BEW/ylr875S4x2BrGo3IuDgcJ34SkBQTyi/vduz6tVHcSt81p9ivRK7EgRERieaoBEVon96iYlmbyznH/Spucin7lci1ODAiIlGcTQQ+2Xi6wzjWO+uYs36tbez4Shz7lcg9OPmaiDrV2URgZ/iEVMc66te22vcc+5XIfXjFqJtYEoS5Kj2uK7GdlaCwCAnwwcmmv35XpN4XCyYMwfi4MJvPjJr/Hl2J7Uq/1rJfmauM4qRokyVBZIYlQUjLxJaguC3WBH1vwNACBPkAFwQJ4AUN58T26/RYE4LZr0Q9wpIgbsKSIMxVLXFdid1ZWYvp75R2+vs6K0Ghhb9HV2LZr8xVqXFKzJUlQdyMJUGYq1rixMSOjg1HlN4PR+ubXVKCQgt/DzGx7FfmqvQ4Kdp0d0kQTr4mok55e+mwcFI8AE4EdiX2K5H8cGBERKKkD43C8ukJiNSztIcrsV+J5IW30ohItPShUbgmPhI7fj7OEhQuxH4lkg8OjIioS7y9dEiKCWUJChdjvxLJA2+lEREREZlxYERERERkxoERERERkRkHRkRERERmnHzdTayVxlyVHidFm8xVXnFStMlc1REnRZuslSYzrJVGRESkXKyV5iaslcZc1RLHXJkrc2WuWjhGSyxrpbkZa6UxV7XESdEmc5VXnBRtMld1xEnRJmulEREREXmI7AdGLS0t2LRpEx555BGMHDkSwcHB8PHxQWRkJCZPnoz169d3GL9x40ZkZGQgLCwM/v7+GDJkCBYsWIBTp0556AiIiIhIKWQ/MNq8eTNSU1Pxj3/8A0eOHMGYMWNw/fXX45xzzkFRURH+9re/4e6774ajqVIvvvgirrnmGhQXF+Piiy/GpEmTUF9fjyVLliAxMRE1NTUSHBERERHJlewHRl5eXpg6dSq2bNmC6upqfP7551i7di1+/PFHfPjhh/D29sabb76J999/3yauvLwc8+bNg7e3N9avX4/Nmzfjo48+wqFDhzB+/HgcOHAA99xzj0RHRURERHIk+4HR1VdfjU8++QQpKSl2r02bNg1ZWVkAgFWrVtm8tnTpUgiCgBkzZmDChAnW7QEBAVixYgW8vLywbt067N+/3635ExERkXLIfmDUmcsuuwwAUFVVZd12+vRp69yjzMxMu5iBAwciOTkZAFBQUOCBLImIiEgJFD8wOnjwIAAgKirKuq2iogJNTU0AgMTERIdxlu3l5eVuzpCIiIiUQtHrGB09ehTvvvsuAGDq1KnW7ZWVlQCA4OBgBAYGOoyNjo622dcZo9EIo9Fo/dlgMABgSZDuxEnRphZy1cIxStEmc5VXnBRtaiFXLRxjV2MUu/L1mTNnkJ6ejk2bNmHYsGEoLS1F7969AQD5+fm49dZbcd555+HIkSMO49966y3MmjULgwcPxoEDB5y2s2jRIuTm5tptZ0kQIiIi5RBbEkSxV4zuuecebNq0Cf369cMnn3xiHRS5Wk5ODrKzs60/GwwGREdHIy0tjSVBmKtH4kytAkoPn8TxBiPCA32RODAE3l66HrfnjlzdFcdcmWtHse76jGihX7VwjJbYwsJCUfsqcmD00EMPYcWKFQgJCcGGDRswePBgm9ctt88aGxud/g7LAo8djRoBwNfXF76+vnbbWRKEuXoirnhvNXKL9qG6vtn6epTeDwsnxSN9aJTTOCly9UScFG0yV3nFtY/1xGdEC/2qhWMUS3GTr+fNm4dXXnkFwcHBKCkpsT6V1tagQYMAAHV1dWhoaHD4eyxPsVn2JZKb4r3VuHd1mc0JHwCO1jfj3tVlKN5bLVFmRPLAzwi5g6IGRo8++iheeOEF6PV6lJSUOH3iLC4uzjr/p7S01OE+lu0JCQnuSZaoB0ytAnKL9sHRBEDLttyifTC1KnKKIFGP8TNC7qKYgdH8+fPx/PPPQ6/XY8OGDRg5cqTTfXv37o2JEycCODtJur3Dhw9j+/btAIApU6a4J2GiHthVWWv3r+C2BADV9c3YVVnruaSIZISfEXIXRQyMnnjiCTz77LMIDg7udFBkMX/+fOh0OqxcuRLFxcXW7U1NTZg5cyZMJhOmTp2KIUOGuDN1IlFMrQJ2VtZid40OOytrcdTg/ITf1vEGcfsRKR0/I+Qpsp98/dlnn+Hpp58GAMTGxiIvL8/hfmFhYfjHP/5h/TkhIQHLli1DdnY2MjIycNVVVyE8PBxbt25FdXU14uLi8Prrr3vkGIg6Yjt51BurDpYitI+4iYXhgX7uTY5IBvgZIU+S/cCotvavy6ClpaVO5wwNHDjQZmAEAHPnzsWwYcOwbNky7Nq1C42NjRgwYABycnKQk5PjdPFHIk+xTB5tPwuitrHjxch0ACL1fhgVE4pW0xm35UckNX5GyNNkPzDKysqyFortjtTUVKSmprouISIX6WjyaFs6wGYfy+osCyfFw9tLh1aTe/Ijkho/IyQF2Q+M5IolQZhrT+N2djJ51CIkwAe1TX/9rki9LxZMGILxcWE270M5HqOr4qRok7lKHyf1Z0St/eqKOCnaZEkQmcnLy0NeXh5MJhMqKipYEoR6bHeNDqsOene63/RYE4J7A4YWIMgHuCBIQJtFfYlUi58RciWxJUE4MOoig8EAvV6PmpoalgRhrl2Ka1+2oLVVwO3v7u70962+MxFJMaEuz7MnsWr4ezBXeeXqqKxH6eGTmP6O43mlbbnrM6KGfnVXnBJzLSwsVHetNKmxJAhz7Uqco7IFkUF+CA7wQX1Ti8M5FJbJo6Njw23qPrk6z57EKvXv4Yk4KdpUaq7Oyno8OTEeUXo/HK1vlvQzotR+9UScFG2yJAiRwjkrW3DM0Iw686Co/Sm9/eRRIrXqqKzH7PwyTB5+tt4ZPyPkKRwYEblRZ2ULdDg7cTQiyLZQcaTeD8unJ9gVwSRSEzFlPT7bU428zMsQqbddj4ifEXIX3kojciMxZQtONrVgzV1JEFpNKNm6E2kpSaJvDRApmdiyHiF9fLHtsaux4+fj/IyQ2/GKEZEbiS1HUHPKiKSYUIwIE5AUE8oTPmmC2M/H8YZmeHvp+Bkhj+AVIyIXalvPqV9lLcL6+nYeBJYtIG3g54OUgAMjIhdxVM8pMshX1JNnLFtAatfTzweRp3BgROQCzuo5HTMYrdtYtoC0yhWfDyJP4Rwjoh4S8+RZcIAPIoL4VA1pDz8fpDS8YtRNrJXGXC06q+ckAKhrasGqrEvg5aWzWdnX20tn144cj1HqOCnaZK6uiXPV58MTuboqToo2eYziYsVgSRCRWCuNnBFbz+n2C00YEcaPG2kLPx8kF6yV5iaslcZc29tZWct6TsyVuTrhqs+HJ3J1VZySctXCMVpiWSvNzVgrjblajI4NZz0nD8VJ0SZz7Vmcqz8f7szV1XFStMlj7DlOvibqIW8vHRZOigfAek5E7fHzQUrDgRGRC6QPjcLy6Qms50TkAD8fpCS8lUbkIulDo3BNfCTrORE5wM8HKQUHRkQuZKnndOIn1nMiao+fD1IC3kojIiIiMuPAiIiIiMiMAyMiIiIiM84x6iaWBGGuSo+Tok3mKq84KdpkruqIk6JNlgSRGZYEISIiUi6WBHETlgRhrmqJY67MlbkyVy0coyWWJUHcjCVBmKta4qRok7nKK06KNpmrOuKkaJMlQYiIiIg8hAMjIiIiIjMOjIiIiIjMODAiIiIiMuPAiIiIiMiMAyMiIiIiMw6MiIiIiMw4MCIiIiIy4wKP3cRaacxV6XFStMlc5RUnRZvMVR1xUrTJWmkyw1pp8tQqAIcMOhhagCAf4IIgAV46qbMiInItnut6jrXS3IS10uST67//ewxPfbEfRw1G636RQb54ImMIrr04Qla5yjGOuTJX5qqMXN15rpPLMXoiV9ZKczPWSpM21+K91Xjgwz1oP6o/ZjDigQ/3YPn0BKQPjZJFrnKPk6JN5iqvOCnaZK7i4jx1rtPK30MMTr4mxTG1Csgt2md3ogBg3ZZbtA+mVl4MJSLl4rlOGooYGB04cACvvvoqsrKyMGzYMPTq1Qs6nQ5PPfVUp7EbN25ERkYGwsLC4O/vjyFDhmDBggU4deqUBzInd9hVWYvq+manrwsAquubsauy1nNJERG5GM910lDErbTly5fj5Zdf7nLciy++iOzsbOh0OqSkpCAiIgJbt27FkiVLsG7dOmzbtg1hYWFuyJjc6XiD8xOF/X7O7yMTEckZz3XSUMQVo6FDh+Lhhx/GmjVr8NNPP+G2227rNKa8vBzz5s2Dt7c31q9fj82bN+Ojjz7CoUOHMH78eBw4cAD33HOPB7KnnjK1CthZWYvdNTrsrKxFWF9fUXHhgX5uzoyIyHV4rpMHRVwxuuuuu2x+9vLqfDy3dOlSCIKAGTNmYMKECdbtAQEBWLFiBc4//3ysW7cO+/fvx5AhQ1yeM7lG8d5q5BbtM19O9saqg6WIDPJFcIAP6ptaHN571wGI1PthVEwoWk1nPJwxEVHX8VwnH4q4YtRVp0+fxvr16wEAmZmZdq8PHDgQycnJAICCggKP5kbiFe+txr2ry+zusR8zGFFnPlG0X8bD8vPCSfHw5iIfRKQAPNfJiyoHRhUVFWhqagIAJCYmOtzHsr28vNxjeZF4nT2NoQMQHOCDiCDbS8iRej+7x1eJiOSK5zr5UcSttK6qrKwEAAQHByMwMNDhPtHR0Tb7OmM0GmE0/rWolsFgAMCSIN2J60rsThFPY9Q1tWBV1iXw8tLheIMR4YG+SBwYAm8vnV07au5XLRyjFG0yV3nFSdGmFs51Wvt7iKHIla+zsrLw3nvvYfHixXjiiSfsXs/Pz8ett96K8847D0eOHHH4O9566y3MmjULgwcPxoEDB5y2tWjRIuTm5jpsgyVB3Gd3jQ6rDnp3ut/tF5owIkxxb2EiIgA813mS2JIgqrxi5Eo5OTnIzs62/mwwGBAdHY20tDSWBHFhrqZWAaWHT1r/NTS+VcCqg7s7/X1pKUlIign1aK5qiWOuzJW5ej7X9ue6VInPdVr6exQWForaV5UDI8vts8bGRqf7WBZ47GjUCAC+vr7w9bV/ZJIlQVyXq+3TGGeJfRpjdGy4qImHWuhXLRyjFG0yV3nFSdGme891frI412nl7yGGKgdGgwYNAgDU1dWhoaHB4Tyjqqoqm31JGpanMRzVAbJs0wE2r/NpDCJSGufnumae62RGlU+lxcXFWef/lJaWOtzHsj0hIcFjeZEtPo1BRFog5lwXEuCDiCDbuxM810lDlVeMevfujYkTJ+Ljjz9Gfn4+xo0bZ/P64cOHsX37dgDAlClTpEiRIK4OUF1TC9bMTIAgmFCydSfSUpJEX1ImIpIDMee6k00tWHNXEoRWnuukpsorRgAwf/586HQ6rFy5EsXFxdbtTU1NmDlzJkwmE6ZOncpVrz2o/XL3R+v/FBVX02hEUkwoRoQJSIoJ5YmCiGTN7lxnEFfzrOYUz3VyoIgrRmVlZbjvvvusPx86dAgA8MYbb+Dzzz+3bi8oKEBU1NlLjgkJCVi2bBmys7ORkZGBq666CuHh4di6dSuqq6sRFxeH119/3bMHomGOlrsP7dNbVCzrABGRUjg+14mbKMxznTwoYmBkMBiwc+dOu+1HjhyxWaeo7UKMADB37lwMGzYMy5Ytw65du9DY2IgBAwYgJycHOTk5Thd/JNdyNunwZOPpDuNYB4iIlMTZua62sePFBXmukxdFDIzGjh2L7q5DmZqaitTUVBdnRGJ1NunQmfZPY7Sa3JAcEZGLdHSua6uzJ894rpOeIgZGcsSSIOLiOlvu3iIkwAcnm/76fZF6XyyYMATj48Js+pr96ro4KdpkrvKKk6JNtebalXNdrYzOdWr9eziLFUORJUGkkJeXh7y8PJhMJlRUVLAkiEhil7u/LdYEfW/A0AIE+QAXBAngvEMiUgqx57rpsSYE81wnCbElQTgw6iKDwQC9Xo+amhqWBBERt7OyFtPfcbyWVFur70zkcvc8Rlm1yVzlFSf3XJV6rlPr38NRbGFhIWuluRNLgoiLGx0bjii9H47WN3O5e5nGSdEmc5VXnBRtqi1XpZ/r1Pb36AnVrmNE8uDtpcPCSfEA/ppkaMHl7olILXiuUw8OjMjt0odGYfn0BETqWdqDiNSL5zp14K008oj0oVG4Jj4SO34+zuXuiUi1eK5TPg6MyGO8vXRIignFiZ+43D0RqRfPdcrGW2lEREREZhwYEREREZlxYERERERkxoERERERkRknX3cTa6UxV6XHSdEmc5VXnBRtMld1xEnRJmulyQxrpRERESkXa6W5CWulMVe1xDFX5spcmasWjtESy1ppbsZaacxVLXFStMlc5RUnRZvMVR1xUrTJWmlEREREHsKBEREREZEZB0ZEREREZhwYEREREZlxYERERERkxoERERERkRkHRkRERERmXMeom1gShLkqPU6KNpmrvOKkaJO5qiNOijZZEkRmWBKEiIhIuVgSxE2UVBLE1Cqg9PBJHG8wIjzQF4kDQ+DtpZNlrnKLU1KuWjhG5spcmau84tzRpru+syyxLAniZnIvCVK8txq5RftQXd9sfT1K74eFk+KRPjRKVrnKOU6KNnmM8mqTucorToo2tZCr1Mfoie8ssTj5WoWK91bj3tVlNm8wADha34x7V5eheG+1RJkRERHZktt3FgdGKmNqFZBbtA+O7o9atuUW7YOplXdQiYhIWnL8zuLASGV2VdbajbrbEgBU1zdjV2Wt55IiIiJyQI7fWRwYqczxBudvsO7sR0RE5C5y/M7i5GuFM7UK2FlZi901OvSrrEVYH19RceGBfm7OjIiIyJYSvrM4MFIw21n83lh1sBSRQX4IDvBBfVOLw3u2OgCRej+MiglFq+mMhzMmIiKtUsp3FgdGCmWZxd/+jXTM0GzdpgNsXresBrFwUjy8vXRoNbk9TSIiIkV9Z3GOkQJ1NotfByAkwAcRQbaXKCP1flg+PcFuTQgiIiJ3Udp3Fq8YdZOUtdJ2ipjFf7KpBe9ljYC3l85uFdH2bbG2jjza5DHKq03mKq84KdrUQq6eOEapv7O6GsOSICJJVSutVQAOGXQwtABBPsAFQQLKT+iw6qB3p7G3X2jCiDD+eYmIyHPaf2/Vnwbe/1n67yzWSnMTT9ZK+/d/j+GpL/bjqMFo3S8yyBfTEvvj5a8Odfo7V9+ZiKSY0C612d1c3R3LXF0fx1yZK3Nlrq4+RkffWyEBPjjZ1PkVG3d9Z1liWSvNzdxdK614bzUe+HCPg4lqRrzy1SFRs/hHx4bbFOBzV67dobU6QHKOk6JN5iqvOCnaZK7qiGsb6+x7q7NBkae+s8Ti5GsZErNEumX2fvu3UPtZ/ERERO7W0fdWW0r4ztLEwOjjjz/G2LFjERISgj59+mD48OF47rnnujWByxPELJF+sqkFc1MvRKTedtErPnlGRESe1tn3lkVIn942P8vxO0v1t9LmzJmDl19+Gb169cLVV1+Nvn374quvvsJjjz2GoqIilJSUwN/fX+o0bYhd+nxQWB9se+xq7Pj5OEq27kRaSpLoS5FERESuIvZ768mJF+Gcvj6y/s5S9cDo008/xcsvv4y+ffti8+bNSEhIAADU1NTg6quvxrZt2/Dkk0/iH//4h6R52i2R3lf8EuneXjokxYTixE8CkmJCZfcGIyIi9enu91ak3h+JA4Jk/Z2l6oHRkiVLAADz58+3DooAICwsDP/85z+RkpKC1157DU8++ST0er0kOTpeIt1X9BLpREREntTT7y25l6NS7Ryj33//Hd999x0AIDMz0+71MWPGIDo6GkajEV988YWn0wPw1xLp7e/LHjMYUWd+cylhohoREWmDFr63VDswKi8vBwCEhoYiJibG4T6JiYk2+3qSmCXSgwN8EBHEydVERCQ9rXxvqfZWWmVlJQBgwIABTveJjo622dcRo9EIo/GvhaoMBgOAnpcEEbNEel1TC1ZlXQKvDpZI70qb3c3VVXFStKmFXLVwjFK0yVzlFSdFm1rItStxrvrekvLvIYZqV75esmQJFixYgOTkZGzbts3hPgsWLMCSJUuQlpaGf//73w73WbRoEXJzc+2297QkyO4alvUgIiLlUPr3ltiSIKq9YuQqOTk5yM7Otv5sMBgQHR2NtLS0HpUE6VdZi1UHSzttPy0lqcMl0rvSptRxzFVeccyVuTJX5tqVOFd9b0n19ygsLBS1r2oHRoGBgQCAxsZGp/ucOnUKADocOfr6+sLX1/4xxJ6WBBkdG44ovR+O1je7pKyHmDblEidFm1rIVQvHKEWbzFVecVK0qYVcxcS5+ntLir+HGKqdfD1o0CAAQFVVldN9LK9Z9vUkby8dFk6KB6D8GfxERKR+WvneUu3A6LLLLgMAnDhxwunk6tLSs5cE265x5EnpQ6OwfHoCy3oQEZEiaOF7S7W30vr374+RI0fiu+++Q35+PhYsWGDz+rZt21BVVQVfX19kZGRIlOXZN9k18ZEs60FERIqg9u8t1V4xAoDHH38cAPDMM8+grKzMuv3EiRO47777AAD333+/ZKteW1jKeowIk+8S6URERBZq/t5S9cDouuuuw4MPPohTp07h8ssvx4QJE3DDDTcgNjYWP/74I5KTk7F48WKp0yQiIiKZUPXACABefvllrF27FqNHj8b27dvxxRdfoH///njmmWfw1Vdfwd/fX+oUiYiISCZUO8eorZtuugk33XST1GkQERGRzGliYOQOPS0J4qo4KdpkruqIk6JN5iqvOCnaZK7qiJOiTZYEkZm8vDzk5eXBZDKhoqKixyVBiIiIyHPElgThwKiLDAYD9Ho9ampqelQSxFVxUrTJXNURx1yZK3Nlrlo4RktsYWEha6W5U09Lgrg6Too2mas64qRok7nKK06KNpmrOuKkaJMlQYiIiIg8hAMjIiIiIjPeSusiy5Qsg8Hg8PWWlhY0NTXBYDB0+d5pd+KkaJO5qiOOuTJX5spctXCMbWOBv77HneHAqIsaGhoAANHR0RJnQkRERF3V0NDQYSkwPpXWRa2trfjjjz8QGBgInc5xbRhL8dquMBgMiI6ORlVVVYez5Z3pTptSxHU3tif94+lcPR3H907H+N7pmBT9o5R+1cpnSyvvncTERHz11Vc499xz4eXlfCYRrxh1kZeXF/r379/hPt7e3t36EAFAUFBQt2K726an43oa253+kSJXKfqV752O8b3TMU/2j5L6FVD/Z0sr751evXp1+v0NcPK1W8yePVsxbXo6rqexnm5PSf3aXUo6Rr53XB/XE0p6D2ihf9g3ronlrTSZsCwc2dnCU1rF/nGOfdMx9k/H2D/OsW86ptb+4RUjmfD19cXChQvh6+srdSqyxP5xjn3TMfZPx9g/zrFvOqbW/uEVIyIiIiIzXjEiIiIiMuPAiIiIiMiMAyMiIiIiMw6MZODjjz/G2LFjERISgj59+mD48OF47rnn0NLSInVqbnXgwAG8+uqryMrKwrBhw9CrVy/odDo89dRTncZu3LgRGRkZCAsLg7+/P4YMGYIFCxbg1KlTHsjc/VpaWrBp0yY88sgjGDlyJIKDg+Hj44PIyEhMnjwZ69ev7zBe7f0DAGvWrMHtt9+O4cOHIzw8HD4+PtDr9Rg1ahSWLl3a4bFqoX/aevTRR6HT6Tr9fGmlX7Kysqz94ey/5uZmh7G7d+/GjTfeiIiICPj5+SEmJgYPPPAAjh8/7uGjcK/Tp0/jlVdewZgxYxAaGgo/Pz/0798fEyZMwNq1ax3GqOb9I5CkHnroIQGA0KtXLyEtLU24/vrrheDgYAGAMGbMGKGpqUnqFN3Gcuzt/1u8eHGHcS+88IIAQNDpdMKVV14p3HjjjUJkZKQAQIiLixP+97//eegI3GfDhg3W/oiMjBQmTpwo3HTTTcLQoUOt22fNmiW0trbaxWqhfwRBEJKTkwWdTifEx8cL1157rXDLLbcIV199teDv7y8AEGJjY4Xff//dLk4r/WPxn//8R/Dy8hJ0Ol2Hny8t9csdd9whABCSk5OFO+64w+F/p0+ftov7+OOPhV69egkAhJEjRwo33XSTcP755wsAhIiICOHgwYMSHI3rVVVVCfHx8QIAISwsTPjb3/4mTJs2TbjiiiuEgIAAYerUqXYxanr/cGAkoYKCAgGA0LdvX2H37t3W7f/73/+EYcOGCQCEefPmSZihe7311lvCww8/LKxZs0b46aefhNtuu63TgVFZWZmg0+kEb29v4YsvvrBub2xsFMaPHy8AcPihVZpNmzYJU6dOFbZs2WL32ocffih4e3sLAIT33nvP5jWt9I8gCMK3334rnDhxwm57TU2NMGbMGAGAcPPNN9u8pqX+EYSzx3XhhRcK5513nnDdddc5/XxprV8sA6OVK1eKjvn999+FgIAAAYDwxhtvWLefOXNGmD59unWw5OgfK0rS1NQkDBkyRAAgLFq0yG6A2NjYKJSXl9tsU9v7hwMjCY0cOVIAIDz11FN2r23dulUAIPj6+gp1dXUSZOd5lpNVRwOjG2+8UQAg3HXXXXav/frrr4KXl5cAQPjpp5/cmarkZs6cKQAQxo8fb7Od/XPWli1bBABCaGiozXat9c+DDz4oABDWr1/f4edLa/3SnYHRI488IgAQUlNT7V5raGgQ9Hq9AEAoLi52Yaae9+STT1qvSIultvcP5xhJ5Pfff7cWwsvMzLR7fcyYMYiOjobRaMQXX3zh6fRk6fTp09a5NY76bODAgUhOTgYAFBQUeDQ3T7vssssAAFVVVdZt7J+/9Op1tgxk24XntNY/33zzDV599VXcfvvtyMjIcLqf1vqluyzH7qiP+vbti8mTJwMA/vWvf3k0L1dqaWnB8uXLAQCPPPKIqBg1vn84MJJIeXk5ACA0NBQxMTEO90lMTLTZV+sqKirQ1NQE4K++aU8rfXbw4EEAQFRUlHUb++eshoYGLFq0CACsX1aAtvrn1KlTuPPOOxEREYGXXnqpw3211C/tff3115g3bx5mzZqFnJwcFBQUwGg02u3X0NCAn3/+GYC6+6isrAw1NTU499xzERsbix9//BG5ubm4++67MX/+fKxfvx6tra02MWp8//SSOgGtqqysBAAMGDDA6T7R0dE2+2qdpR+Cg4MRGBjocB8t9NnRo0fx7rvvAgCmTp1q3a7V/ikpKUF+fj5aW1tx7Ngx7NixAw0NDUhPT8ezzz5r3U9L/fPwww+jsrISBQUFCAkJ6XBfLfVLe6tWrbLbFhUVhXfeeQfp6enWbb/++qv1/52ds9XQRz/88AMAoH///pg/fz6ee+45CG2KYzz77LO47LLL8Omnn1r7QY3vH14xkkhDQwMAoE+fPk736du3L4CzhfqIfQYAZ86cwfTp01FfX49hw4bh7rvvtr6m1f7Zt28f3nvvPbz//vsoKSlBQ0MDMjMz8e6770Kv11v300r/lJSU4I033sDNN9+M6667rtP9tdIvbQ0fPhwvv/wy9u7dC4PBgGPHjqGkpARXXHEFqqurMXnyZHzzzTfW/S19BDjvJzX00YkTJwCcvbLz7LPP4r777sOBAwdQX1+PDRs2YPDgwSgvL8fEiROty8mo8f3DgRGRgtxzzz3YtGkT+vXrh08++QS9e/eWOiXJzZkzB4Ig4PTp0/j555+xbNkyfPnll4iPj8eWLVukTs+j6uvrMXPmTJxzzjl49dVXpU5HtubOnYsHH3wQF198MQIDAxEeHo5rrrkG27Ztw9///ne0tLRgzpw5UqfpcZarQy0tLbjlllvw2muvYfDgwQgKCkJqaio2bNgAPz8/7N27Fx9++KHE2boPB0YSsVxybGxsdLqPZVGsoKAgj+Qkd1rvs4ceeggrVqxASEiI9V9vbWm9f3x8fHDBBRcgOzsbX375JU6ePInp06fjzz//BKCN/pkzZw6OHDmC1157DWFhYaJitNAvYul0OuTm5gIA9uzZY324oe0tImf9pIY+anucba9GWwwYMAATJ04EcHYxx7Yxanr/cGAkkUGDBgGwfaqoPctrln21ztIPdXV1Npe221Jrn82bNw+vvPIKgoODUVJSYn0qrS0t9097SUlJiI+PR1VVFUpLSwFoo38KCgrQq1cv/POf/8TYsWNt/isuLgYArFixAmPHjsXNN98MQBv90hUXXXSR9f+PHDkC4OyTVRa//fabwzg19NH555/v8P8d7VNdXQ1Ane8fDowkYvliO3HihNMJaZYTekJCgsfykrO4uDgEBAQA+Ktv2lNjnz366KN44YUXoNfrUVJS4vTJD632jzOWOQ+WUg1a6Z8zZ85g8+bNdv8dO3YMwNmJxJs3b8a3334LQDv9IpZlng3w19WQoKAgxMbGAlB3HyUkJECn0wEAampqHO5j2W6ZN6TG9w8HRhLp378/Ro4cCQDIz8+3e33btm2oqqqCr69vh2uQaEnv3r2tl3Ed9dnhw4exfft2AMCUKVM8mpu7zJ8/H88//zz0ej02bNhgfc84osX+caampgZ79uwBAOstRy30T11dHYSzC/fa/XfHHXcAABYvXgxBEKxPWmmhX7rCMncmKCgIcXFx1u2WY3fUR6dOnUJRUREA4Prrr/dAlu4RGRmJMWPGAPjrVllbLS0t2Lx5MwBg1KhRAFT6/pFqZUlyXhKkpqZGEyVB2hOz8vXu3butS89/+eWX1u1KXXq+IwsWLBAACMHBwcKuXbtExWilf/773/8Kq1evFv7880+71w4cOCCMHTtWACBcfvnlNq9ppX8c6ejzpaV+KS8vFwoLC4WWlhab7SaTSXj77bcFPz8/AYDwxBNP2LzetiTIm2++ad1+5swZazkjNZQE2bhxowBACAkJEXbs2GHd3tLSIjzwwAMCACEwMFA4evSo9TW1vX84MJKYZcl+Hx8fIT09XZg6daq1iGxycrKqi8ju3r1bSEpKsv4XFhYmABD69+9vs/2PP/6wiWtbrHDs2LHCTTfdJERFRSmyWKEzhYWF1mKxiYmJTgtdOho4a6F/vv76awGA0KdPH2HMmDHCzTffLFx//fVCYmKitfzARRddJBw+fNguVgv940hn//DQSr9Y/kEaEhIijB8/XsjMzBQyMjKEAQMGWD9zt9xyi93ASRAE4aOPPrLWKUxKShKmTZumyiKyixcvFmAubn7FFVcI119/vTBo0CABgODv7y98/vnndjFqev9wYCQDa9euFa688kohKChI8Pf3F4YOHSo888wzgtFolDo1t7J8uXX2X2VlpV3shg0bhPT0dCE0NFTw9fUVLrzwQiEnJ0cwGAyePxA3WLlypai+GThwoMN4tffP8ePHhaefflpIT08XBg0aJPTp00fo3bu3EBkZKVxzzTXC8uXLhebmZqfxau8fR8RckdVCv/zyyy/CnDlzhDFjxgjnnXee4OfnJ/j6+goDBgwQbrjhBmH9+vUdxpeWlgrXX3+9cM455wi9e/cWBg4cKMyePdvmCooa/Pvf/xYmTJgghIaGCj4+PkJ0dLSQlZXVYb0ztbx/dILQZllLIiIiIg3j5GsiIiIiMw6MiIiIiMw4MCIiIiIy48CIiIiIyIwDIyIiIiIzDoyIiIiIzDgwIiIiIjLjwIiIiIjIjAMjIiIiIjMOjIiIiIjMODAiIiIiMuPAiIiIiMiMAyMiIiIiMw6MiEiznnrqKeh0Olx++eUOX58/fz50Oh0uvfRSnDx50sPZEZEUdIIgCFInQUQkhT///BODBw/GkSNH8Mknn2Dq1KnW15YuXYrHH38ccXFx2LJlC8LDwyXMlIg8hVeMiEiz/P398fTTTwMAFixYgDNnzgAAli9fjscffxwxMTHYtGkTB0VEGsIrRkSkaYIgIDExEWVlZXj99dfRt29f3HbbbTj33HOxdetWxMTESJ0iEXkQB0ZEpHnffPMNxo0bh5CQEDQ0NCAkJARbtmzBkCFDpE6NiDyMAyMiIgDJycnYvn07AgMDsWXLFlx66aVSp0REEuAcIyLSvJUrV2LHjh0AAKPRiKCgIIkzIiKpcGBERJr28ccf4//+7/8QGhqKadOm4fTp03jsscekTouIJMJbaUSkWV988QWuu+46+Pv746uvvsIFF1yACy64ALW1tfjPf/6DK664QuoUicjDeMWIiDRp8+bNuOGGG9CrVy8UFRVhxIgRCA4OxuOPPw4AyM7OljhDIpICrxgRkebs2rULqampMBqNKCwsRHp6uvU1o9GIuLg4HD58GB988AFuvvlmCTMlIk/jFSMi0pQff/wREyZMQFNTE9asWWMzKAIAX19fLF68GACQk5MDo9EoRZpEJBFeMSIiIiIy4xUjIiIiIjMOjIiIiIjMODAiIiIiMuPAiIiIiMiMAyMiIiIiMw6MiIiIiMw4MCIiIiIy48CIiIiIyIwDIyIiIiIzDoyIiIiIzDgwIiIiIjLjwIiIiIjI7P8DvdLzJKtutrQAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -627,7 +529,7 @@ "\n", "\n", "qmod = create_model(main)\n", - "qmod = update_constraints(qmod, optimization_parameter=\"width\")\n", + "qmod = update_constraints(qmod, optimization_parameter=OptimizationParameter.WIDTH)\n", "\n", "# synthesize\n", "qprog = synthesize(qmod)\n", @@ -664,16 +566,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "d1bbd003-27fc-4d1e-9ecc-f7cbdd512831", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:28.927858Z", - "iopub.status.busy": "2024-05-07T14:48:28.926609Z", - "iopub.status.idle": "2024-05-07T14:48:28.973762Z", - "shell.execute_reply": "2024-05-07T14:48:28.972878Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "@qfunc\n", @@ -697,25 +592,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "2b893be2-6d70-4264-a84b-38ec318f83c8", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:28.979004Z", - "iopub.status.busy": "2024-05-07T14:48:28.977800Z", - "iopub.status.idle": "2024-05-07T14:48:32.516515Z", - "shell.execute_reply": "2024-05-07T14:48:32.515714Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening: https://platform.classiq.io/circuit/af819a46-e820-471f-9c62-65af85eccd18?version=0.41.0.dev39%2B79c8fd0855\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "qprog = synthesize(qmod)\n", "show(qprog)\n", @@ -725,16 +605,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "a5ef7dbb-a01c-401a-9c16-d9d9d17f9a6c", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:32.521320Z", - "iopub.status.busy": "2024-05-07T14:48:32.520096Z", - "iopub.status.idle": "2024-05-07T14:48:32.536778Z", - "shell.execute_reply": "2024-05-07T14:48:32.536046Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "matrix_of_ind_v = get_independent_set(samples)\n", @@ -746,26 +619,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "36311726-fb1d-40d7-b830-ab4af4e2ea84", - "metadata": { - "execution": { - "iopub.execute_input": "2024-05-07T14:48:32.541426Z", - "iopub.status.busy": "2024-05-07T14:48:32.540171Z", - "iopub.status.idle": "2024-05-07T14:48:32.547594Z", - "shell.execute_reply": "2024-05-07T14:48:32.546952Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The secret binary string (integer) of f(x): 60\n", - "The result of the Simon's Algorithm: 60\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "s_secret = int(\"1\" * PARTITION_INDEX + \"0\" * (NUM_QUBITS - PARTITION_INDEX), 2)\n", "print(\"The secret binary string (integer) of f(x):\", s_secret)\n", diff --git a/algorithms/simon/simon_example.synthesis_options.json b/algorithms/simon/simon_example.synthesis_options.json index 31c9776e8..33438e123 100644 --- a/algorithms/simon/simon_example.synthesis_options.json +++ b/algorithms/simon/simon_example.synthesis_options.json @@ -1,5 +1,43 @@ { "constraints": { + "max_gate_count": {}, "optimization_parameter": "width" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "cy", + "rx", + "u", + "u1", + "p", + "z", + "cz", + "sdg", + "y", + "s", + "u2", + "tdg", + "rz", + "id", + "r", + "h", + "cx", + "sx", + "sxdg", + "t", + "x", + "ry" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 4217120978 } } diff --git a/algorithms/simon/simon_shallow_example.synthesis_options.json b/algorithms/simon/simon_shallow_example.synthesis_options.json index 0967ef424..0e0333520 100644 --- a/algorithms/simon/simon_shallow_example.synthesis_options.json +++ b/algorithms/simon/simon_shallow_example.synthesis_options.json @@ -1 +1,43 @@ -{} +{ + "constraints": { + "max_gate_count": {}, + "optimization_parameter": "no_opt" + }, + "preferences": { + "machine_precision": 8, + "custom_hardware_settings": { + "basis_gates": [ + "cy", + "rx", + "u", + "u1", + "p", + "z", + "cz", + "sdg", + "y", + "s", + "u2", + "tdg", + "rz", + "id", + "r", + "h", + "cx", + "sx", + "sxdg", + "t", + "x", + "ry" + ], + "is_symmetric_connectivity": true + }, + "debug_mode": true, + "synthesize_all_separately": false, + "output_format": ["qasm"], + "pretty_qasm": true, + "transpilation_option": "auto optimize", + "timeout_seconds": 300, + "random_seed": 4002322088 + } +} diff --git a/requirements_tests.txt b/requirements_tests.txt index 54d71fdd5..b40ea218a 100644 --- a/requirements_tests.txt +++ b/requirements_tests.txt @@ -1,7 +1,6 @@ pytest testbook - torch torchvision galois From 36d41199291040cacd793a00aa62843624784a51 Mon Sep 17 00:00:00 2001 From: Or Samimi Golan Date: Tue, 24 Dec 2024 14:21:28 +0200 Subject: [PATCH 37/38] fixed control with variable in both arguments issue --- .../option_pricing/option_pricing.ipynb | 41 ++++++++++--------- .../option_pricing/option_pricing.qmod | 9 +++- .../option_pricing.synthesis_options.json | 36 ++++++++-------- .../QMOD_workshop/QMOD_Workshop_Part_2.ipynb | 28 +++++++++---- 4 files changed, 66 insertions(+), 48 deletions(-) diff --git a/applications/finance/option_pricing/option_pricing.ipynb b/applications/finance/option_pricing/option_pricing.ipynb index 2362ea370..64296694c 100644 --- a/applications/finance/option_pricing/option_pricing.ipynb +++ b/applications/finance/option_pricing/option_pricing.ipynb @@ -164,7 +164,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "id": "986b9ce8", "metadata": { "pycharm": { @@ -277,12 +277,11 @@ "\n", "@qfunc\n", "def payoff(asset: QNum, ind: QBit):\n", - " control(\n", - " asset\n", - " >= ceiling(\n", - " descale(K)\n", - " ), # check if asset price is 'in the money' - crossed the strike price\n", - " lambda: payoff_linear(asset, ind),\n", + " aux = QBit(\"aux\")\n", + " # check if asset price is 'in the money' - crossed the strike price\n", + " within_apply(\n", + " lambda: assign(asset >= ceiling(descale(K)), aux),\n", + " lambda: control(aux, lambda: payoff_linear(asset, ind)),\n", " )" ] }, @@ -300,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "f6e1682d-75ec-4765-945a-6d1f8501365b", "metadata": {}, "outputs": [], @@ -351,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "bd2d44b5", "metadata": { "pycharm": { @@ -404,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "id": "a7fdde0f-8413-43ef-b7d5-788b86cfdc54", "metadata": {}, "outputs": [], @@ -435,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "9e9e68fa-afe7-4668-b37b-1b6c385d9ce2", "metadata": {}, "outputs": [], @@ -451,14 +450,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "id": "d912597d-c196-43c8-9955-fa4ccd3cbe99", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", - "text": [] + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/8d3aaafa-a271-4384-8588-1e9499bf8cf7?version=0.64.0.dev0\n" + ] } ], "source": [ @@ -476,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "486b167a-b67b-4e2f-8ea3-ed5faa9f4e9e", "metadata": {}, "outputs": [], @@ -495,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "id": "7b257b85-4f48-4625-8c1f-f05895e60575", "metadata": {}, "outputs": [ @@ -503,8 +504,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Measured Payoff: 0.17755290428851778\n", - "Confidence Interval: [0.1727416 0.18236421]\n" + "Measured Payoff: 0.17567552705951653\n", + "Confidence Interval: [0.17237516 0.17897589]\n" ] } ], @@ -526,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "id": "4acddc0a-83c2-480a-bf68-975c4bd0fcc6", "metadata": {}, "outputs": [ @@ -545,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "id": "d30f73bb-feda-4043-94f2-916728d910fc", "metadata": {}, "outputs": [], diff --git a/applications/finance/option_pricing/option_pricing.qmod b/applications/finance/option_pricing/option_pricing.qmod index 07dbfcdbd..6a6632e29 100644 --- a/applications/finance/option_pricing/option_pricing.qmod +++ b/applications/finance/option_pricing/option_pricing.qmod @@ -56,8 +56,13 @@ qfunc payoff_linear(asset: qnum, ind: qbit) { } qfunc payoff(asset: qnum, ind: qbit) { - control (asset >= ceiling(12.9502500662)) { - payoff_linear(asset, ind); + aux: qbit; + within { + aux = asset >= ceiling(12.9502500662); + } apply { + control (aux) { + payoff_linear(asset, ind); + } } } diff --git a/applications/finance/option_pricing/option_pricing.synthesis_options.json b/applications/finance/option_pricing/option_pricing.synthesis_options.json index 1b330aa64..7b26d76dd 100644 --- a/applications/finance/option_pricing/option_pricing.synthesis_options.json +++ b/applications/finance/option_pricing/option_pricing.synthesis_options.json @@ -8,36 +8,38 @@ "machine_precision": 8, "custom_hardware_settings": { "basis_gates": [ - "sx", - "rx", + "id", + "cx", "u", - "z", - "cz", - "y", - "s", - "u1", + "sdg", + "t", "p", - "r", - "cy", + "u1", "ry", - "rz", + "z", "x", - "h", - "t", - "sxdg", "tdg", - "cx", - "id", + "y", + "cz", "u2", - "sdg" + "sxdg", + "rx", + "s", + "rz", + "cy", + "sx", + "r", + "h" ], "is_symmetric_connectivity": true }, "debug_mode": true, + "synthesize_all_separately": false, + "optimization_level": 3, "output_format": ["qasm"], "pretty_qasm": true, "transpilation_option": "auto optimize", "timeout_seconds": 300, - "random_seed": -1 + "random_seed": 4290148293 } } diff --git a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb index 835ab2561..dd84a742e 100644 --- a/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb +++ b/tutorials/workshops/QMOD_workshop/QMOD_Workshop_Part_2.ipynb @@ -678,18 +678,24 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2024-10-07T13:39:26.331226Z", "start_time": "2024-10-07T13:39:26.326156Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening: https://nightly.platform.classiq.io/circuit/6fec16d5-72a1-49bf-ab24-3287255c3391?version=0.64.0.dev0\n" + ] + } + ], "source": [ "# Solution to Exercise 9:\n", - "\n", - "\n", "from classiq import *\n", "\n", "\n", @@ -699,10 +705,14 @@ " allocate(3, x)\n", " hadamard_transform(x)\n", "\n", - " control(\n", - " x < 0.5,\n", - " stmt_block=lambda: inplace_xor(2.0 * x + 1.0, res),\n", - " else_block=lambda: inplace_xor(1.0 * x + 0.5, res),\n", + " aux = QBit(\"aux\")\n", + " within_apply(\n", + " lambda: assign(x < 0.5, aux),\n", + " lambda: control(\n", + " aux,\n", + " stmt_block=lambda: inplace_xor(2.0 * x + 1.0, res),\n", + " else_block=lambda: inplace_xor(1.0 * x + 0.5, res),\n", + " ),\n", " )\n", "\n", "\n", @@ -773,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.4" }, "vscode": { "interpreter": { From a0fa1413a9b390a470455728235be8f9ee560ca3 Mon Sep 17 00:00:00 2001 From: AnnePicus Date: Tue, 24 Dec 2024 16:38:58 +0200 Subject: [PATCH 38/38] English suggestions to Introducing Quantum Functions with Quantum Monte Carlo Integration --- .../qmc_user_defined/qmc_user_defined.ipynb | 108 +++++++++--------- 1 file changed, 51 insertions(+), 57 deletions(-) diff --git a/algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.ipynb b/algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.ipynb index f54391b9e..5b88ac02b 100644 --- a/algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.ipynb +++ b/algorithms/amplitude_estimation/qmc_user_defined/qmc_user_defined.ipynb @@ -5,9 +5,9 @@ "id": "456a591a-6383-45cf-ac3e-cca3014edf6b", "metadata": {}, "source": [ - "# Introducing quantum functions with Quantum Monte Carlo Integration\n", + "# Introducing Quantum Functions with Quantum Monte Carlo Integration\n", "\n", - "In this tutorial we introduce how to write custom quantum functions with Classiq, and subsequently use them for more complex functions/algorithms. This will be illustrated on a specific use-case of Quantum Monte Carlo Integration (QMCI). The example below demonstrates how we can exploit various concepts of modeling quantum algorithms with Classiq when building our own functions." + "This tutorial explains how to write custom quantum functions with Classiq and subsequently uses them for more complex functions or algorithms. This is illustrated on a specific use case of Quantum Monte Carlo Integration (QMCI). The example below demonstrates how we can exploit various concepts of modeling quantum algorithms with Classiq when building our own functions." ] }, { @@ -25,7 +25,7 @@ "\\tag{1}\n", "E_{p}(x) = \\int f(x)p(x) dx.\n", "\\end{equation}\n", - "Such evaluations appear in the context of option-pricing or credit risk-analysis.\n", + "Such evaluations appear in the context of option pricing or credit risk analysis.\n", "\n", "The basic idea of QMCI assumes that we have a quantum function $A$, which, for a given $f$ and $p$, loads the following state of $n+1$ qubits:\n", "\\begin{align}\n", @@ -33,18 +33,18 @@ "A|0\\rangle_n|0\\rangle = \\sum^{2^n-1}_{i=0} \\sqrt{f_i} \\sqrt{p_i}|i\\rangle_n|1\\rangle + \\sum^{2^n-1}_{i=0} \\sqrt{1-f_i} \\sqrt{p_i}|i\\rangle_n|0\\rangle = \\sqrt{a}|\\psi_1\\rangle+\\sqrt{1-a^2}|\\psi_0\\rangle,\n", "\\end{align}\n", "where it is understood that the first $2^n$ states represent a discretized space of $x$, and that $0\\leq f(x)\\leq 1$.\n", - "Then, by applying Amplitude Estimation (AE) algorithm for the \"good-state\" $|\\psi_1 \\rangle$, we can estimate its amplitude\n", + "Then, by applying the amplitude estimation (AE) algorithm for the \"good-state\" $|\\psi_1 \\rangle$, we can estimate its amplitude:\n", "$$\n", "a = \\sum^{2^n-1}_{i=0} f_i p_i.\n", "$$\n", "\n", "The QMCI algorithm can be separated into two parts:\n", - "1) Constructing a Grover operator for the specific problem--- this will be done here almost from scratch.\n", - "2) Applying AE algorithm based on the Grover operator [[1](#AE)]--- this will be done by calling Classiq's Quantum Phase Estimation (QPE) function.\n", + "1) Constructing a Grover operator for the specific problem. This is done here almost from scratch.\n", + "2) Applying the AE algorithm based on the Grover operator [[1](#AE)]. This is done by calling the Classiq Quantum Phase Estimation (QPE) function.\n", "\n", - "### Specific use-case for the tutorial\n", + "### Specific Use Case for the Tutorial\n", "\n", - "For simplicity we will consider a simple use-case. We take a probability distribution on the integers\n", + "For simplicity we consider a simple use case. We take a probability distribution on the integers\n", "$$\n", "\\tag{3}\n", "p_i = \\frac{i}{\\mathcal{N}} \\text{ for } i\\in \\{0,\\dots 2^3-1\\},\n", @@ -54,7 +54,7 @@ "\\tag{4}\n", "f(x) = \\sin^2(0.25x+0.2).\n", "$$\n", - "Therefore, the value we want to evaluate is:\n", + "Therefore, the value we want to evaluate is\n", "$$\n", "a= \\frac{1}{\\mathcal{N}} \\sum^7_{k=0} \\sin^2(0.25k+0.2) k \\approx 0.834.\n", "$$" @@ -65,16 +65,16 @@ "id": "c810e0d5-6fda-4868-aab9-ff036ff8974e", "metadata": {}, "source": [ - "## 1. Building the corresponding Grover Operator \n", + "## 1. Building the Corresponding Grover Operator \n", "\n", "### Quantum Functions\n", "\n", - "The following example will demonstrate how to define QMOD functions by writing a Python function decorated with the `@qfunc` decorator.\n", + "This example demonstrates how to define Qmod functions by writing a Python function decorated with the `@qfunc` decorator.\n", "The typical workflow for defining a quantum function:\n", - "1. Specifying the function signature: The `@qfunc` decorator relies on Python's type-hint mechanism to extract the signature of the QMOD function from the argument list of the Python function.\n", - "2. Specifying the function body: A function decorated with `@qfunc` is executed by the Python interpreter to construct the body of the QMOD function. Inside it, you can do one of the following:\n", - " - Call other `@qfuncs` to insert the corresponding quantum function calls into the body of the resulting QMOD function\n", - " - Introduce local quantum variables, by instantiating a quantum type\n", + "1. Specifying the function signature: The `@qfunc` decorator relies on Python's type-hint mechanism to extract the signature of the Qmod function from the argument list of the Python function.\n", + "2. Specifying the function body: To construct the body of the Qmod function, the Python interpreter executes a function decorated with `@qfunc`. Inside, you can do one of these:\n", + " - Call other `@qfuncs` to insert the corresponding quantum function calls into the body of the resulting Qmod function\n", + " - Introduce local quantum variables by instantiating a quantum type\n", " - Use arithmetic and in-place assignment operators to insert special quantum statements into the function\n", " " ] @@ -84,7 +84,7 @@ "id": "d259adad-9b69-4602-932b-97d98b546503", "metadata": {}, "source": [ - "We can start with relevant imports" + "We can start with relevant imports:" ] }, { @@ -106,7 +106,7 @@ "id": "c2be12ee-3d17-49df-a69f-efab41b60b29", "metadata": {}, "source": [ - "### Grover operator for QMCI\n", + "### Grover Operator for QMCI\n", "\n", "The Grover operator suitable for QMCI is defined as follows:\n", "$$\n", @@ -114,7 +114,7 @@ "$$\n", "with $S_0$ and $S_{\\psi_1}$ being reflection operators around the zero state $|0\\rangle_n|0\\rangle$ and the good-state $|\\psi_1\\rangle$, respectively, and the function $A$ is defined in Eq. ([2](#mjx-eqn-2)).\n", "\n", - "In subsections (1.1)-(1.3) below we build each of the quantum sub-functions, and then in subsection (1.4) we combine them to define a complete Grover operator. On the way we introduce several concepts of functional modeling which allow Classiq's Synthesis Engine to reach better optimized circuits. " + "In subsections (1.1)-(1.3) below we build each of the quantum sub-functions, and then in subsection (1.4) we combine them to define a complete Grover operator. On the way we introduce several concepts of functional modeling, which allow the Classiq synthesis engine to reach better optimized circuits. " ] }, { @@ -122,9 +122,9 @@ "id": "a2c31065-077a-475a-ba06-af9b10a396d5", "metadata": {}, "source": [ - "#### 1.1) The state loading $A$ function\n", + "#### 1.1) The State Loading $A$ Function\n", "\n", - "We start with constructing the $A$ operator in Eq. ([2](#mjx-eqn-2)). We define a quantum function and give it the name `state_loading`" + "We start with constructing the $A$ operator in Eq. ([2](#mjx-eqn-2)). We define a quantum function and give it the name `state_loading`." ] }, { @@ -133,7 +133,7 @@ "metadata": {}, "source": [ "The function's signature declares two arguments: \n", - "1. A quantum register `io` declared as `QArray[QBit]` (an array of qubits with an unspecified size): will be used to represent the discretization of space\n", + "1. A quantum register `io` declared as `QArray[QBit]` (an array of qubits with an unspecified size) that is used to represent the discretization of space.\n", "2. A quantum register `ind` of size 1 declared as `QBit` to indicate the good state. " ] }, @@ -143,19 +143,19 @@ "metadata": {}, "source": [ "Next, we construct the logic flow of the `state_loading` function. \n", - "The function body consists of 2 quantum function calls: `load_probabilities` followed by `amplitude_loading`\n", + "The function body consists of two quantum function calls:\n", "\n", - "- As can be seen from Eq. ([2](#mjx-eqn-2)), the `load_probabilities` function is constructed using Classiq's `inplace_prepare_state` function call on $n=3$ qubits with probabilities $p_i$ \n", - "- The `amplitude_loading` body consists of a function call to Classiq's `linear_pauli_rotations`. The `linear_pauli_rotations` is used to load the amplitude of the function $ f(x) = sin^2(0.25 x + 0.2) $.\n", + "1. As can be seen from Eq. ([2](#mjx-eqn-2)), the `load_probabilities` function is constructed using the Classiq `inplace_prepare_state` function call on $n=3$ qubits with probabilities $p_i$. \n", + "2. The `amplitude_loading` body calls the Classiq `linear_pauli_rotations` function. The `linear_pauli_rotations` loads the amplitude of the function $ f(x) = sin^2(0.25 x + 0.2) $.\n", "\n", - " *Note: the amplitude should be $sin$ so the probability would be $sin^2$.*\n", + " *Note: The amplitude should be $sin$ so the probability is $sin^2$.*\n", "\n", - " The function uses an auxiliary qubit that is utilized so that the desired probability will reflect on the auxiliary qubit if it is in the `|1>` state.\n", + " The function uses an auxiliary qubit that is utilized so that the desired probability reflects on the auxiliary qubit if it is in the `|1>` state.\n", "\n", - " We will use the function with the Pauli Y matrix and enter the appropriate slope and offset to achieve the right parameters.\n", + " We use the function with the Pauli Y matrix and enter the appropriate slope and offset to achieve the right parameters.\n", "\n", "\n", - "We will define the probabilities according to our specific problem described by Eqs. ([3](#mjx-eqn-3)-[4](#mjx-eqn-4))" + "We define the probabilities according to the specific problem described by Eqs. ([3](#mjx-eqn-3)-[4](#mjx-eqn-4))." ] }, { @@ -200,7 +200,7 @@ "id": "d06ba0e3-bbac-45d4-8ff5-46158b4038c8", "metadata": {}, "source": [ - "To examine our function we define a quantum `main` function from which we can build a model, synthesize and view the quantum program created:" + "To examine our function we define a quantum `main` function from which we can build a model, synthesize, and view the quantum program created:" ] }, { @@ -214,9 +214,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "" - ] + "text": [] } ], "source": [ @@ -237,9 +235,9 @@ "id": "59b38acb-9ca9-4cfd-b87a-4208c75c63ca", "metadata": {}, "source": [ - "#### 1.2) $S_{\\psi_1}$ function - The good state oracle\n", + "#### 1.2) $S_{\\psi_1}$ Function - The Good State Oracle\n", "\n", - "The next quantum function we define is the one which reflects around the good state: any $n+1$ state in which the `ind` register is at state $|1\\rangle$. This function can be simply constructed with a ZGate on the `ind` register. \n" + "The next quantum function we define is the one that reflects around the good state: any $n+1$ state in which the `ind` register is at state $|1\\rangle$. This function can be constructed with a ZGate on the `ind` register. \n" ] }, { @@ -261,13 +259,13 @@ "id": "fcc22b6c-8c2d-4ac9-ba63-c66416d40af9", "metadata": {}, "source": [ - "#### 1.3) $S_{0}$ function - The Grover Diffuser\n", + "#### 1.3) $S_{0}$ Function - The Grover Diffuser\n", "\n", - "In order to implement the Grover Diffuser we aim to perform a controlled-Z operation on the $|0>^n$ state.\n", + "To implement the Grover Diffuser we aim to perform a controlled-Z operation on the $|0>^n$ state.\n", "\n", "We can define a `zero_oracle` quantum function with the `io` and `ind` registers as its arguments. \n", "\n", - "The `within_apply` operator takes two function arguments - compute and action, and invokes the sequence compute(), action(), and invert(compute()). Quantum objects that are allocated and prepared by compute are subsequently uncomputed and released.\n", + "The `within_apply` operator takes two function arguments—compute and action—and invokes the sequence `compute()`, `action()`, and `invert(compute())`. Quantum objects that are allocated and prepared by compute are subsequently uncomputed and released.\n", "\n", "The `control` condition is a logical expression over a quantum variable. Currently, expressions are restricted to the form ` == `, where both `` and `` are integer types." ] @@ -295,7 +293,7 @@ "id": "a8a9636f-0007-4ca8-98d5-6a1ce7002820", "metadata": {}, "source": [ - "One can verify that:\n", + "We can verify that\n", "\\begin{eqnarray}\n", "|00\\dots0\\rangle \\xrightarrow[{\\rm ctrl(-Z)(target=q_0, ctrl=q_1\\dots q_n)}]{} -|00\\dots0\\rangle, \\\\\n", "|10\\dots0\\rangle \\xrightarrow[{\\rm ctrl(-Z)(target=q_0, ctrl=q_1\\dots q_n)}]{} |10\\dots0\\rangle, \\\\\n", @@ -311,12 +309,12 @@ "id": "52d45da1-8090-4e60-beed-9e4b3c57d929", "metadata": {}, "source": [ - "#### 1.4) $Q$ function - The Grover operator\n", + "#### 1.4) $Q$ Function - The Grover Operator\n", "\n", - "We can now define a complete Grover operator $Q\\equiv -S_{\\psi_1} A^{\\dagger} S_0 A$. We will do this in a single code block that will call the following:\n", + "We can now define a complete Grover operator $Q\\equiv -S_{\\psi_1} A^{\\dagger} S_0 A$. We do this in a single code block that calls the following:\n", "1. The good state oracle (`good_state_oracle`)\n", "2. THe inverse of the state preparation (`state_loading`)\n", - "3. The Diffuser (`zero_oracle`)\n", + "3. The diffuser (`zero_oracle`)\n", "4. The state preparation (`state_loading`)\n", " \n", "*Note:*\n", @@ -352,7 +350,7 @@ "id": "0f4ffdde-0c92-436a-a28c-65cf843162de", "metadata": {}, "source": [ - "##### Let us look at the `my_grover_operator` function we created" + "##### Let us look at the `my_grover_operator` function we created:" ] }, { @@ -366,9 +364,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "" - ] + "text": [] } ], "source": [ @@ -394,14 +390,14 @@ "source": [ "## 2. Applying Amplitude Estimation (AE) with Quantum Phase Estimation (QPE)\n", "\n", - "Below we apply a basic AE algorithm which is based on QPE. The idea behind this Algorithm is the following:\n", + "Here we apply a basic AE algorithm that is based on QPE. The idea behind this algorithm is the following:\n", "\n", "The state $A|0\\rangle_n|0\\rangle$ is spanned by two eigenvectors of our Grover operator $Q$, with the two corresponding eigenvalues\n", "\\begin{equation}\n", "\\tag{5}\n", "\\lambda_{\\pm}=\\exp\\left(\\pm i2\\pi \\theta \\right), \\qquad \\sin^2 \\left(\\pi \\theta\\right)\\equiv a.\n", "\\end{equation}\n", - "Therefore, if we apply a QPE on $A|0\\rangle_n|0\\rangle$ we will have these two eigenvalues encoded in the QPE register, however, both give the value of $a$, so there is no ambiguity here." + "Therefore, if we apply a QPE on $A|0\\rangle_n|0\\rangle$, we have these two eigenvalues encoded in the QPE register. However, both give the value of $a$, so there is no ambiguity." ] }, { @@ -409,7 +405,7 @@ "id": "225566be-8c41-4d7a-abc6-ef3bb83a885b", "metadata": {}, "source": [ - "To find $a$ we are going to build a simple quantum model: we apply $A$ on a quantum register of size $n+1$ initialized to zero, and then apply Classiq's QPE with the `my_grover_operator` we defined." + "To find $a$ we build a simple quantum model, applying $A$ on a quantum register of size $n+1$ initialized to zero, and then applying the Classiq QPE with the `my_grover_operator` we defined." ] }, { @@ -417,7 +413,7 @@ "id": "e0605069-5062-4f01-92f8-a6b599c7e4bd", "metadata": {}, "source": [ - "Below is the `main` function from which we can build our model and synthesize it. In particular, we define the output register `phase` as `QNum` to hold the phase register output of the QPE. We choose a QPE with phase register of size 3, governing the accuracy of our Phase-, and thus Amplitude-, Estimation. " + "Below is the `main` function from which we can build our model and synthesize it. In particular, we define the output register `phase` as `QNum` to hold the phase register output of the QPE. We choose a QPE with phase register of size 3, governing the accuracy of our phase-, and thus amplitude-, estimation. " ] }, { @@ -429,9 +425,7 @@ { "name": "stdout", "output_type": "stream", - "text": [ - "" - ] + "text": [] } ], "source": [ @@ -463,7 +457,7 @@ "id": "14f3bf9f-4740-4849-896d-b9cb0dd064cb", "metadata": {}, "source": [ - "We can simply export our model to a `.qmod` file:" + "We can export our model to a `.qmod` file:" ] }, { @@ -481,9 +475,9 @@ "id": "94b452a3-7a47-440d-9c9a-bf88c9f5d3fd", "metadata": {}, "source": [ - "### Finally, we execute the circuit and measure the approximated amplitude\n", + "### Executing the Circuit and Measuring the Approximated Amplitude\n", "\n", - "We start with a simple execution on a simulator" + "We execute on a simulator:" ] }, { @@ -525,7 +519,7 @@ "id": "cee12720-1205-40d6-970f-eb36e76911ad", "metadata": {}, "source": [ - "Plotting the resulting histogram we see two phase values with high probability (however, both corresponds to the same amplitude $a$)" + "Upon plotting the resulting histogram we see two phase values with high probability (however, both correspond to the same amplitude $a$):" ] }, { @@ -565,7 +559,7 @@ "id": "e75fe2d0-3e27-48e6-b8ee-0b9a33b7eb12", "metadata": {}, "source": [ - "Recall the relation in Eq. ([5](#mjx-eqn-5)), we can read the amplitude $a$ from the phase with max probability, and compare to the expected amplitude:" + "Recalling the relation in Eq. ([5](#mjx-eqn-5)), we can read the amplitude $a$ from the phase with maximum probability and compare to the expected amplitude:" ] }, {