[PR1/? from #774] Upgrade Cirq to >=1.0

docs/tutorials/hello_many_worlds.ipynb

@@ -255,7 +255,7 @@
         "# Create a circuit on these qubits using the parameters you created above.\n",
         "circuit = cirq.Circuit(\n",
         "    cirq.rx(a).on(q0),\n",
-        "    cirq.ry(b).on(q1), cirq.CNOT(control=q0, target=q1))\n",
+        "    cirq.ry(b).on(q1), cirq.CNOT(q0, q1))\n",
         "\n",
         "SVGCircuit(circuit)"
       ]

docs/tutorials/qcnn.ipynb

@@ -554,7 +554,7 @@
         "    source_basis_selector = one_qubit_unitary(source_qubit, symbols[3:6])\n",
         "    pool_circuit.append(sink_basis_selector)\n",
         "    pool_circuit.append(source_basis_selector)\n",
-        "    pool_circuit.append(cirq.CNOT(control=source_qubit, target=sink_qubit))\n",
+        "    pool_circuit.append(cirq.CNOT(source_qubit, sink_qubit))\n",
         "    pool_circuit.append(sink_basis_selector**-1)\n",
         "    return pool_circuit"
       ]

docs/tutorials/research_tools.ipynb

@@ -83,7 +83,8 @@
    },
    "outputs": [],
    "source": [
-    "!pip install tensorflow==2.7.0 tensorflow-quantum==0.7.2 tensorboard_plugin_profile==2.4.0"
+    "!pip install tensorflow==2.7.0 tensorflow-quantum==0.7.2 tensorboard_plugin_profile==2.4.0\n",
+    "!pip install --quiet git+https://github.com/quantumlib/ReCirq"
    ]
   },
     {
@@ -124,6 +125,7 @@
     "import datetime\n",
     "import time\n",
     "import cirq\n",
+    "from recirq import beyond_classical\n",
     "import tensorflow as tf\n",
     "import tensorflow_quantum as tfq\n",
     "from tensorflow.keras import layers\n",
@@ -155,7 +157,7 @@
    "source": [
     "def generate_circuit(qubits):\n",
     "    \"\"\"Generate a random circuit on qubits.\"\"\"\n",
-    "    random_circuit = cirq.generate_boixo_2018_supremacy_circuits_v2(\n",
+    "    random_circuit = beyond_classical.generate_boixo_2018_beyond_classical_v2(\n",
     "        qubits, cz_depth=2, seed=1234)\n",
     "    return random_circuit\n",
     "\n",

release/setup.py

@@ -51,7 +51,7 @@ def finalize_options(self):
 
 
 REQUIRED_PACKAGES = [
-    'cirq-core==0.13.1', 'cirq-google>=0.13.1', 'sympy == 1.8',
+    'cirq-core~=1.0', 'cirq-google~=1.0', 'sympy == 1.8',
     'googleapis-common-protos==1.52.0', 'google-api-core==1.21.0',
     'google-auth==1.18.0', 'protobuf==3.19.4'
 ]

requirements.txt

@@ -1,5 +1,5 @@
-cirq-core==0.13.1
-cirq-google==0.13.1
+cirq-core~=1.0
+cirq-google~=1.0
 sympy==1.8
 numpy==1.24.2  # TensorFlow can detect if it was built against other versions.
 nbformat==4.4.0

scripts/ci_validate_tutorials.sh

@@ -24,6 +24,8 @@ pip install gym==0.24.1
 pip install seaborn==0.12.0
 # tf_docs pip package needed for noise tutorial.
 pip install -q git+https://github.com/tensorflow/docs
+# ReCirq pip package needed for research tools.
+pip install --quiet git+https://github.com/quantumlib/ReCirq
 # Leave the quantum directory, otherwise errors may occur
 cd ..
 examples_output=$(python3 quantum/scripts/test_tutorials.py)

tensorflow_quantum/core/ops/circuit_execution_ops_test.py

@@ -27,6 +27,7 @@
 from scipy import stats
 import cirq
 import cirq_google
+from cirq_google.engine.abstract_processor import AbstractProcessor
 
 from tensorflow_quantum.core.ops import batch_util, circuit_execution_ops
 from tensorflow_quantum.python import util
@@ -97,11 +98,9 @@ def test_get_expectation_inputs(self):
         circuit_execution_ops.get_expectation_op()
         with self.assertRaisesRegex(NotImplementedError,
                                     expected_regex='Sample-based'):
-            mock_engine = mock.Mock()
+            mock_processor = mock.create_autospec(AbstractProcessor)
             circuit_execution_ops.get_expectation_op(
-                cirq_google.QuantumEngineSampler(engine=mock_engine,
-                                                 processor_id='test',
-                                                 gate_set=cirq_google.XMON))
+                cirq_google.ProcessorSampler(processor=mock_processor))
         with self.assertRaisesRegex(
                 TypeError,
                 expected_regex="cirq.sim.simulator.SimulatesExpectationValues"):
@@ -118,11 +117,9 @@ def test_get_sampled_expectation_inputs(self):
             backend=cirq.Simulator())
         circuit_execution_ops.get_sampled_expectation_op(
             backend=cirq.DensityMatrixSimulator())
-        mock_engine = mock.Mock()
+        mock_processor = mock.create_autospec(AbstractProcessor)
         circuit_execution_ops.get_sampled_expectation_op(
-            cirq_google.QuantumEngineSampler(engine=mock_engine,
-                                             processor_id='test',
-                                             gate_set=cirq_google.XMON))
+            cirq_google.ProcessorSampler(processor=mock_processor))
         with self.assertRaisesRegex(TypeError, expected_regex="a Cirq.Sampler"):
             circuit_execution_ops.get_sampled_expectation_op(backend="junk")
 
@@ -137,11 +134,9 @@ def test_get_samples_inputs(self):
         circuit_execution_ops.get_sampling_op(backend=cirq.Simulator())
         circuit_execution_ops.get_sampling_op(
             backend=cirq.DensityMatrixSimulator())
-        mock_engine = mock.Mock()
+        mock_processor = mock.create_autospec(AbstractProcessor)
         circuit_execution_ops.get_sampling_op(
-            backend=cirq_google.QuantumEngineSampler(engine=mock_engine,
-                                                     processor_id='test',
-                                                     gate_set=cirq_google.XMON))
+            backend=cirq_google.ProcessorSampler(processor=mock_processor))
         with self.assertRaisesRegex(TypeError,
                                     expected_regex="Expected a Cirq.Sampler"):
             circuit_execution_ops.get_sampling_op(backend="junk")
@@ -161,12 +156,9 @@ def test_get_state_inputs(self):
             circuit_execution_ops.get_state_op(backend="junk")
         with self.assertRaisesRegex(TypeError,
                                     expected_regex="Cirq.SimulatesFinalState"):
-            mock_engine = mock.Mock()
+            mock_processor = mock.create_autospec(AbstractProcessor)
             circuit_execution_ops.get_state_op(
-                backend=cirq_google.QuantumEngineSampler(
-                    engine=mock_engine,
-                    processor_id='test',
-                    gate_set=cirq_google.XMON))
+                backend=cirq_google.ProcessorSampler(processor=mock_processor))
 
         with self.assertRaisesRegex(TypeError,
                                     expected_regex="must be type bool."):

tensorflow_quantum/core/ops/cirq_ops.py

@@ -491,7 +491,7 @@ def _no_grad(grad):
         ]
         max_n_qubits = max(len(p.all_qubits()) for p in programs)
 
-        if isinstance(sampler, cirq_google.QuantumEngineSampler):
+        if isinstance(sampler, cirq_google.ProcessorSampler):
             # group samples from identical circuits to reduce communication
             # overhead. Have to keep track of the order in which things came
             # in to make sure the output is ordered correctly

tensorflow_quantum/core/ops/cirq_ops_test.py

@@ -16,6 +16,7 @@
 # Remove PYTHONPATH collisions for protobuf.
 # pylint: disable=wrong-import-position
 import sys
+
 NEW_PATH = [x for x in sys.path if 'com_google_protobuf' not in x]
 sys.path = NEW_PATH
 # pylint: enable=wrong-import-position
@@ -26,6 +27,7 @@
 from absl.testing import parameterized
 import cirq
 import cirq_google
+from cirq_google.engine.abstract_processor import AbstractProcessor
 
 from tensorflow_quantum.core.ops import cirq_ops
 from tensorflow_quantum.core.serialize import serializer
@@ -348,11 +350,9 @@ def test_get_cirq_sampling_op(self):
         cirq_ops._get_cirq_samples()
         cirq_ops._get_cirq_samples(cirq.Simulator())
         cirq_ops._get_cirq_samples(cirq.DensityMatrixSimulator())
-        mock_engine = mock.Mock()
+        mock_processor = mock.create_autospec(AbstractProcessor)
         cirq_ops._get_cirq_samples(
-            cirq_google.QuantumEngineSampler(engine=mock_engine,
-                                             processor_id='test',
-                                             gate_set=cirq_google.XMON))
+            cirq_google.ProcessorSampler(processor=mock_processor))
 
     def test_cirq_sampling_op_inputs(self):
         """test input checking in the cirq sampling op."""
@@ -451,7 +451,9 @@ class DummySampler(cirq.Sampler):
             def run_sweep(self, program, params, repetitions):
                 """Returns all ones in the correct sample shape."""
                 return [
-                    cirq.Result(
+                    cirq_google.EngineResult(
+                        job_id="1",
+                        job_finished_time="1",
                         params=param,
                         measurements={
                             'tfq':

tensorflow_quantum/core/serialize/op_deserializer_test.py

@@ -38,7 +38,7 @@ def op_proto(json_dict):
 
 
 @cirq.value_equality
-class GateWithAttribute(cirq.SingleQubitGate):
+class GateWithAttribute(cirq.testing.SingleQubitGate):
     """GateAttribute helper class."""
 
     def __init__(self, val, not_req=None):

tensorflow_quantum/core/serialize/op_serializer_test.py

@@ -38,14 +38,14 @@ def op_proto(json):
     return op
 
 
-class GateWithAttribute(cirq.SingleQubitGate):
+class GateWithAttribute(cirq.testing.SingleQubitGate):
     """GateAttribute helper class."""
 
     def __init__(self, val):
         self.val = val
 
 
-class GateWithProperty(cirq.SingleQubitGate):
+class GateWithProperty(cirq.testing.SingleQubitGate):
     """GateProperty helper class."""
 
     def __init__(self, val, not_req=None):
@@ -58,7 +58,7 @@ def val(self):
         return self._val
 
 
-class GateWithMethod(cirq.SingleQubitGate):
+class GateWithMethod(cirq.testing.SingleQubitGate):
     """GateMethod helper class."""
 
     def __init__(self, val):

tensorflow_quantum/python/optimizers/rotosolve_minimizer_test.py

@@ -145,7 +145,7 @@ def convert_to_circuit(input_data):
         a, b = sympy.symbols('a b')  # parameters for the circuit
         circuit = cirq.Circuit(
             cirq.rx(a).on(q0),
-            cirq.ry(b).on(q1), cirq.CNOT(control=q0, target=q1))
+            cirq.ry(b).on(q1), cirq.CNOT(q0, q1))
 
         # Build the Keras model.
         model = tf.keras.Sequential([

tensorflow_quantum/python/optimizers/spsa_minimizer_test.py

@@ -248,7 +248,7 @@ def convert_to_circuit(input_data):
         a, b = sympy.symbols('a b')  # parameters for the circuit
         circuit = cirq.Circuit(
             cirq.rx(a).on(q0),
-            cirq.ry(b).on(q1), cirq.CNOT(control=q0, target=q1))
+            cirq.ry(b).on(q1), cirq.CNOT(q0, q1))
 
         # Build the Keras model.
         model = tf.keras.Sequential([