pygsti.algorithms.germselection.compute_germ_set_score

pygsti.algorithms.germselection.compute_germ_set_score#

compute_germ_set_score(germs, target_model=None, neighborhood=None, neighborhood_size=5, randomization_strength=0.01, score_func='all', op_penalty=0.0, l1_penalty=0.0, num_nongauge_params=None, float_type=None, gate_penalty=None)#

Calculate the score of a germ set with respect to a model.

More precisely, this function computes the maximum score (roughly equal to the number of amplified parameters) for a cloud of models. If target_model is given, it serves as the center of the cloud, otherwise the cloud must be supplied directly via neighborhood.

Parameters:

  • germs (list) – The germ set

  • target_model (Model, optional) – The target model, used to generate a neighborhood of randomized models.

  • neighborhood (list of Models, optional) – The “cloud” of models for which scores are computed. If not None, this overrides target_model, neighborhood_size, and randomization_strength.

  • neighborhood_size (int, optional) – Number of randomized models to construct around target_model.

  • randomization_strength (float, optional) – Strength of unitary randomizations, as passed to target_model.randomize_with_unitary().

  • score_func ({'all', 'worst'}) – Sets the objective function for scoring the eigenvalues. If ‘all’, score is sum(1/input_array). If ‘worst’, score is 1/min(input_array).

  • op_penalty (float, optional) – Coefficient for a penalty linear in the sum of the germ lengths.

  • l1_penalty (float, optional) – Coefficient for a penalty linear in the number of germs.

  • num_nongauge_params (int, optional) – Force the number of nongauge parameters rather than rely on automated gauge optimization.

  • float_type (numpy dtype object, optional) – Numpy data type to use for floating point arrays.

  • gate_penalty (dict, optional (default None)) – An optional dictionary allowing the specification of gate-specific penalties to add for each instance of the specified gate(s) in each germ. Should be specified as a dictionary whose keys are strings corresponding to gate names, and whose values are the penalty factor to add for each instance of that gate. E.g. {‘Gcnot’:2} would correspond to a penalty term where each instance of a ‘Gcnot’ gate gets and additional 2 units added to the cost function for a candidate germ.

Returns:

The maximum score for germs, indicating how many parameters it amplifies.

Return type:

CompositeScore