def test_simulator_bell_pair_result_types(bell_ir_with_result, targets,
                                          batch_size):
    simulator = DefaultSimulator()
    ir = bell_ir_with_result(targets)
    result = simulator.run(ir, qubit_count=2, shots=0, batch_size=batch_size)
    assert len(result.resultTypes) == 2
    assert result.resultTypes[0] == ResultTypeValue.construct(
        type=ir.results[0], value={"11": complex(1 / 2**0.5)})
    assert result.resultTypes[1] == ResultTypeValue.construct(
        type=ir.results[1], value=(0 if targets else [0, 0]))
    assert result.taskMetadata == TaskMetadata(
        id=result.taskMetadata.id,
        deviceId=DefaultSimulator.DEVICE_ID,
        shots=0)
    assert result.additionalMetadata == AdditionalMetadata(action=ir)
Ejemplo n.º 2
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def result_str_4(task_metadata_zero_shots, additional_metadata):
    result = GateModelTaskResult(
        resultTypes=[
            ResultTypeValue(type=jaqcd.Probability(targets=[0]), value=[0.5, 0.5]),
            ResultTypeValue(
                type=jaqcd.StateVector(), value=[(0.70710678, 0), (0, 0), (0, 0), (0.70710678, 0)]
            ),
            ResultTypeValue(type=jaqcd.Expectation(observable=["y"], targets=[0]), value=0.0),
            ResultTypeValue(type=jaqcd.Variance(observable=["y"], targets=[0]), value=0.1),
            ResultTypeValue(type=jaqcd.Amplitude(states=["00"]), value={"00": (0.70710678, 0)}),
        ],
        measuredQubits=list(range(2)),
        taskMetadata=task_metadata_zero_shots,
        additionalMetadata=additional_metadata,
    )
    return result.json()
Ejemplo n.º 3
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 def _calculate_result_types(
         ir_string: str, measurements: np.ndarray,
         measured_qubits: List[int]) -> List[ResultTypeValue]:
     ir = json.loads(ir_string)
     result_types = []
     if not ir.get("results"):
         return result_types
     for result_type in ir["results"]:
         ir_observable = result_type.get("observable")
         observable = observable_from_ir(
             ir_observable) if ir_observable else None
         targets = result_type.get("targets")
         rt_type = result_type["type"]
         if rt_type == "probability":
             value = GateModelQuantumTaskResult._probability_from_measurements(
                 measurements, measured_qubits, targets)
             casted_result_type = Probability(targets=targets)
         elif rt_type == "sample":
             value = GateModelQuantumTaskResult._calculate_for_targets(
                 GateModelQuantumTaskResult._samples_from_measurements,
                 measurements,
                 measured_qubits,
                 observable,
                 targets,
             )
             casted_result_type = Sample(targets=targets,
                                         observable=ir_observable)
         elif rt_type == "variance":
             value = GateModelQuantumTaskResult._calculate_for_targets(
                 GateModelQuantumTaskResult._variance_from_measurements,
                 measurements,
                 measured_qubits,
                 observable,
                 targets,
             )
             casted_result_type = Variance(targets=targets,
                                           observable=ir_observable)
         elif rt_type == "expectation":
             value = GateModelQuantumTaskResult._calculate_for_targets(
                 GateModelQuantumTaskResult._expectation_from_measurements,
                 measurements,
                 measured_qubits,
                 observable,
                 targets,
             )
             casted_result_type = Expectation(targets=targets,
                                              observable=ir_observable)
         else:
             raise ValueError(f"Unknown result type {rt_type}")
         result_types.append(
             ResultTypeValue.construct(type=casted_result_type,
                                       value=value))
     return result_types
    def _generate_results(
        circuit_ir: Program,
        non_observable_result_types: Dict[int, ResultType],
        observable_result_types: Dict[int, ObservableResultType],
        observables: List[Observable],
        simulation,
    ) -> List[ResultTypeValue]:

        results = [0] * len(circuit_ir.results)

        for index in non_observable_result_types:
            results[index] = ResultTypeValue.construct(
                type=circuit_ir.results[index],
                value=non_observable_result_types[index].calculate(simulation),
            )

        if observable_result_types:
            simulation.apply_observables(observables)
            for index in observable_result_types:
                results[index] = ResultTypeValue.construct(
                    type=circuit_ir.results[index],
                    value=observable_result_types[index].calculate(simulation),
                )
        return results
Ejemplo n.º 5
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def result_obj_4(task_metadata_zero_shots, additional_metadata):
    return GateModelTaskResult(
        resultTypes=[
            ResultTypeValue.construct(type=jaqcd.Probability(targets=[0]),
                                      value=np.array([0.5, 0.5])),
            ResultTypeValue.construct(
                type=jaqcd.StateVector(),
                value=np.array(
                    [complex(0.70710678, 0), 0, 0,
                     complex(0.70710678, 0)]),
            ),
            ResultTypeValue.construct(type=jaqcd.Expectation(observable=["y"],
                                                             targets=[0]),
                                      value=0.0),
            ResultTypeValue.construct(type=jaqcd.Variance(observable=["y"],
                                                          targets=[0]),
                                      value=0.1),
            ResultTypeValue.construct(type=jaqcd.Amplitude(states=["00"]),
                                      value={"00": complex(0.70710678, 0)}),
        ],
        measuredQubits=list(range(2)),
        taskMetadata=task_metadata_zero_shots,
        additionalMetadata=additional_metadata,
    )