pygsti.tools.likelihoodfns.logl_max_per_circuit

pygsti.tools.likelihoodfns.logl_max_per_circuit#

logl_max_per_circuit(model, dataset, circuits=None, poisson_picture=True, op_label_aliases=None, mdc_store=None)#

The vector of maximum log-likelihood contributions for each circuit, aggregated over outcomes.

Parameters:

  • model (Model) – the model, used only for circuit compilation

  • dataset (DataSet) – the data set to use.

  • circuits (list of (tuples or Circuits), optional) – Each element specifies a circuit to include in the max-log-likelihood sum. Default value of None implies all the circuits in dataset should be used.

  • poisson_picture (boolean, optional) – Whether the Poisson-picture maximum log-likelihood should be returned.

  • op_label_aliases (dictionary, optional) – Dictionary whose keys are operation label “aliases” and whose values are tuples corresponding to what that operation label should be expanded into before querying the dataset. Defaults to the empty dictionary (no aliases defined) e.g. op_label_aliases[‘Gx^3’] = (‘Gx’,’Gx’,’Gx’)

  • mdc_store (ModelDatasetCircuitsStore, optional) – An object that bundles cached quantities along with a given model, dataset, and circuit list. If given, model and dataset and circuits should be set to None.

Returns:

Array of length either len(circuits) or len(dataset.keys()). Values are the maximum log-likelihood contributions of the corresponding circuit aggregated over outcomes.

Return type:

numpy.ndarray