quara.interface.cvxpy.qtomography.standard.estimator module

class CvxpyLossMinimizationEstimationResult(estimated_var_sequence, computation_times, template_qoperation, estimated_loss_sequence=None)[source]

Bases: quara.protocol.qtomography.standard.loss_minimization_estimator.LossMinimizationEstimationResult

Constructor

Parameters

  • computation_times (List[float]) – computation times for each estimate.

  • estimated_var_sequence (List[numpy.ndarray]) –

  • template_qoperation (quara.objects.qoperation.QOperation) –

  • estimated_loss_sequence (List[float]) –

property estimated_loss_sequence: List[float]

returns sequence of estimated loss.

Returns

sequence of estimated loss.

Return type

List[float]

class CvxpyLossMinimizationEstimator[source]

Bases: quara.protocol.qtomography.standard.loss_minimization_estimator.LossMinimizationEstimator

calc_estimate(qtomography, empi_dists, loss, loss_option, algo, algo_option, is_computation_time_required=False)[source]

calculates estimate variables. Notice: this function updates loss and algo properties.

Parameters

  • qtomography (StandardQTomography) – StandardQTomography to calculates estimate variables.

  • empi_dists (List[Tuple[int, np.ndarray]]) – empirical distributions to calculates estimate variables.

  • loss (CvxpyLossFunction) – ProbabilityBasedLossFunction to calculates estimate variables.

  • loss_option (CvxpyLossFunctionOption) – ProbabilityBasedLossFunctionOption to calculates estimate variables.

  • algo (CvxpyMinimizationAlgorithm) – MinimizationAlgorithm to calculates estimate variables.

  • algo_option (CvxpyMinimizationAlgorithmOption) – MinimizationAlgorithmOption to calculates estimate variables.

  • is_computation_time_required (bool, optional) – whether to include computation time in the return value or not, by default False.

Returns

estimation result.

Return type

CvxpyLossMinimizationEstimationResult

Raises

  • ValueError – loss.is_option_sufficient() returns False.

  • ValueError – algo.is_loss_sufficient() returns False.

  • ValueError – algo.is_option_sufficient() returns False.

  • ValueError – algo.is_loss_and_option_sufficient() returns False.

calc_estimate_sequence(qtomography, empi_dists_sequence, loss, loss_option, algo, algo_option, is_computation_time_required=False)[source]

calculates sequence of estimate variables. Notice: this function updates loss and algo properties.

Parameters

  • qtomography (StandardQTomography) – StandardQTomography to calculates estimate variables.

  • empi_dists_sequence (List[List[Tuple[int, np.ndarray]]]) – sequence of empirical distributions to calculates estimate variables.

  • loss (CvxpyLossFunction) – ProbabilityBasedLossFunction to calculates estimate variables.

  • loss_option (CvxpyLossFunctionOption) – ProbabilityBasedLossFunctionOption to calculates estimate variables.

  • algo (CvxpyMinimizationAlgorithm) – MinimizationAlgorithm to calculates estimate variables.

  • algo_option (CvxpyMinimizationAlgorithmOption) – MinimizationAlgorithmOption to calculates estimate variables.

  • is_computation_time_required (bool, optional) – whether to include computation time in the return value or not, by default False.

Returns

estimation result.

Return type

CvxpyLossMinimizationEstimationResult

Raises

  • ValueError – loss.is_option_sufficient() returns False.

  • ValueError – algo.is_loss_sufficient() returns False.

  • ValueError – algo.is_option_sufficient() returns False.

  • ValueError – algo.is_loss_and_option_sufficient() returns False.