Added a option to store the transpiled circuit in the benchmark direc…

…troy. Readded transpile option for now.

src/modules/applications/QML/generative_modeling/data/data_handler/DataHandler.py

@@ -18,6 +18,7 @@
 import numpy as np
 import pandas as pd
 from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+from qiskit import qpy
 
 from modules.Core import *
 from utils import start_time_measurement, end_time_measurement
@@ -102,6 +103,10 @@ def postprocess(self, input_data: dict, config: dict, **kwargs):
             with open(f"{store_dir_iter}/samples_complete_{kwargs['rep_count']}.pkl", 'wb') as f:
                 pickle.dump(input_data.pop("samples_complete"), f)
 
+        if "circuit_transpiled" in list(input_data.keys()):
+            with open(f"{store_dir_iter}/transpiled_circuit_{kwargs['rep_count']}.qpy", 'wb') as f:
+                qpy.dump(input_data.pop("circuit_transpiled"), f)
+
         evaluation, _ = self.evaluate(input_data)
 
         if self.generalization_mark is not None:

src/modules/applications/QML/generative_modeling/mappings/Library.py

@@ -67,14 +67,14 @@ def preprocess(self, input_data: dict, config: Config, **kwargs):
         output = self.sequence_to_circuit(input_data)
         backend = self.select_backend(config["backend"])
         try:
-            output["execute_circuit"] = self.get_execute_circuit(
+            output["execute_circuit"], output['circuit_transpiled'] = self.get_execute_circuit(
                 output["circuit"],
                 backend,
                 config["backend"],
                 config["n_shots"],
                 config["transpile_optimization_level"])
         except:
-            output["execute_circuit"] = self.get_execute_circuit(
+            output["execute_circuit"], output['circuit_transpiled'] = self.get_execute_circuit(
             output["circuit"],
             backend,
             config["backend"],

src/modules/applications/QML/generative_modeling/mappings/LibraryQiskit.py

@@ -283,6 +283,9 @@ def get_execute_circuit(circuit: QuantumCircuit, backend: Backend, config: str,
         """
         n_qubits = circuit.num_qubits
 
+        #readded this option, since this variable will be stored
+        circuit_transpiled = transpile(circuit, backend=backend, optimization_level=transpile_optimization_level, seed_transpiler=42)
+
         if transpile_optimization_level == -1:
             from mqt.predictor.rl import Predictor, qcompile
             pred = Predictor(figure_of_merit="expected_fidelity", device_name="ibm_guadalupe")
@@ -331,10 +334,14 @@ def execute_circuit(solutions):
                 if transpile_optimization_level in range(4):
                     all_circuit_transpiled = [transpile(circuit, backend=backend, optimization_level=transpile_optimization_level) for circuit in all_circuits]
 
+
+
                 elif transpile_optimization_level==-1:
                     all_circuit_transpiled = [qcompile(circuit, figure_of_merit="expected_fidelity", device_name="ibm_guadalupe", predictor_singleton=pred) [0] for circuit in all_circuits]
 
                 qobjs = assemble(all_circuit_transpiled, backend=backend)
+                #circuit_transpiled = qobjs; if possible
+
                 jobs = backend.run(qobjs, shots=n_shots)
                 samples_dictionary = [jobs.result().get_counts(circuit).int_outcomes() for circuit in all_circuits]
 
@@ -351,4 +358,4 @@ def execute_circuit(solutions):
                 pmfs = samples / n_shots
                 return pmfs, samples
 
-        return execute_circuit
+        return execute_circuit, circuit_transpiled

src/modules/applications/QML/generative_modeling/transformations/PIT.py

@@ -132,6 +132,8 @@ def reverse_transform(self, input_data: dict) -> (any, float):
         n_registers = self.transform_config["n_registers"]
         KL_best_transformed = min(input_data["KL"])
         best_results = input_data["best_sample"]
+        circuit_transpiled = input_data['circuit_transpiled']
+
 
         array_bins = self.compute_discretization_efficient(n_qubits, n_registers)
         transformed_samples = self.generate_samples_efficient(best_results, array_bins, n_registers, noisy=True)
@@ -169,7 +171,8 @@ def reverse_transform(self, input_data: dict) -> (any, float):
             "histogram_generated_original": histogram_generated_original,
             "histogram_generated": histogram_generated_transformed,
             "KL_best_transformed": KL_best_transformed,
-            "store_dir_iter": input_data["store_dir_iter"]
+            "store_dir_iter": input_data["store_dir_iter"],
+            "circuit_transpiled": circuit_transpiled
         }
 
         return reverse_config_trans