def QCBMCostFunction(
ansatz: Ansatz,
backend: QuantumBackend,
distance_measure: Callable,
distance_measure_parameters: dict,
target_bitstring_distribution: BitstringDistribution,
gradient_type: str = "finite_difference",
):
"""Cost function used for evaluating QCBM.
Args:
ansatz (zquantum.core.interfaces.ansatz.Ansatz): the ansatz used to construct the variational circuits
backend (zquantum.core.interfaces.backend.QuantumBackend): backend used for QCBM evaluation
distance_measure (callable): function used to calculate the distance measure
distance_measure_parameters (dict): dictionary containing the relevant parameters for the chosen distance measure
target_bitstring_distribution (zquantum.core.bitstring_distribution.BitstringDistribution): bistring distribution which QCBM aims to learn
gradient_type (str): parameter indicating which type of gradient should be used.
Returns:
Callable that evaluates the parametrized circuit produced by the ansatz with the given parameters and returns
the distance between the produced bitstring distribution and the target distribution
"""
assert (int(target_bitstring_distribution.get_qubits_number()) ==
ansatz.number_of_qubits)
def cost_function(parameters: np.ndarray,
store_artifact: StoreArtifact = None) -> ValueEstimate:
"""
Evaluates the value of the cost function for given parameters.
Args:
parameters: parameters for which the evaluation should occur.
Returns:
(float): cost function value for given parameters
zquantum.core.bitstring_distribution.BitstringDistribution: distribution obtained
"""
circuit = ansatz.get_executable_circuit(parameters)
distribution = backend.get_bitstring_distribution(circuit)
value = evaluate_distribution_distance(
target_bitstring_distribution,
distribution,
distance_measure,
distance_measure_parameters=distance_measure_parameters,
)
if store_artifact:
store_artifact("bitstring_distribution", distribution)
return ValueEstimate(value)
if gradient_type == "finite_difference":
cost_function = function_with_gradient(
cost_function, finite_differences_gradient(cost_function))
else:
raise RuntimeError("Unsupported gradient type: ", gradient_type)
return cost_function
def _create_QCBM_cost_function(
ansatz: Ansatz,
backend: QuantumBackend,
n_samples: int,
distance_measure: DistanceMeasure,
distance_measure_parameters: dict,
target_bitstring_distribution: BitstringDistribution,
):
assert (int(target_bitstring_distribution.get_qubits_number()) ==
ansatz.number_of_qubits)
def cost_function(parameters: np.ndarray,
store_artifact: StoreArtifact = None) -> ValueEstimate:
"""
Evaluates the value of the cost function for given parameters.
Args:
parameters: parameters for which the evaluation should occur.
store_artifact: callable defining how the bitstring distributions should be stored.
"""
# TODO: we use private method here due to performance reasons.
# This should be fixed once better mechanism for handling it will be implemented.
# In case of questions ask mstechly.
# circuit = ansatz.get_executable_circuit(parameters)
circuit = ansatz._generate_circuit(parameters)
distribution = backend.get_bitstring_distribution(circuit, n_samples)
value = evaluate_distribution_distance(
target_bitstring_distribution,
distribution,
distance_measure,
distance_measure_parameters=distance_measure_parameters,
)
if store_artifact:
store_artifact("bitstring_distribution", distribution)
return ValueEstimate(value)
return cost_function