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)
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)
def cost_function(params):
# build the ansatz circuit
qcbm_circuit = build_qcbm_circuit_ion_trap(n_qubits,
params,
single_qubit_gate,
static_entangler,
topology=topology)
measured_distr = backend.get_bitstring_distribution(qcbm_circuit)
distribution_history.append(measured_distr)
value_estimate = ValueEstimate(
evaluate_distribution_distance(
target_bitstring_distribution,
measured_distr,
distance_measure,
epsilon=epsilon,
))
return value_estimate.value
def _evaluate(self, parameters: np.ndarray) -> float:
"""
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 = build_ansatz_circuit(self.ansatz, parameters)
distribution = self.backend.get_bitstring_distribution(circuit)
value = evaluate_distribution_distance(
self.target_bitstring_distribution,
distribution,
self.distance_measure,
epsilon=self.epsilon)
return value, distribution