pygsti.algorithms.core.run_lgst

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pygsti.algorithms.core.run_lgst#

run_lgst(dataset, prep_fiducials, effect_fiducials, target_model, op_labels=None, op_label_aliases=None, guess_model_for_gauge=None, svd_truncate_to=None, verbosity=0, check=True)#

Performs Linear-inversion Gate Set Tomography on the dataset.

Parameters:

  • dataset (DataSet) – The data used to generate the LGST estimates

  • prep_fiducials (list of Circuits) – Fiducial Circuits used to construct a informationally complete effective preparation.

  • effect_fiducials (list of Circuits) – Fiducial Circuits used to construct a informationally complete effective measurement.

  • target_model (Model) – A model used to specify which operation labels should be estimated, a guess for which gauge these estimates should be returned in.

  • op_labels (list, optional) – A list of which operation labels (or aliases) should be estimated. Overrides the operation labels in target_model. e.g. [‘Gi’,’Gx’,’Gy’,’Gx2’]

  • op_label_aliases (dictionary, optional) – Dictionary whose keys are operation label “aliases” and whose values are circuits 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’] = pygsti.baseobjs.Circuit([‘Gx’,’Gx’,’Gx’])

  • guess_model_for_gauge (Model, optional) – A model used to compute a gauge transformation that is applied to the LGST estimates before they are returned. This gauge transformation is computed such that if the estimated gates matched the model given, then the operation matrices would match, i.e. the gauge would be the same as the model supplied. Defaults to target_model.

  • svd_truncate_to (int, optional) – The Hilbert space dimension to truncate the operation matrices to using a SVD to keep only the largest svdToTruncateTo singular values of the I_tildle LGST matrix. Zero means no truncation. Defaults to dimension of target_model.

  • verbosity (int, optional) – How much detail to send to stdout.

  • check (bool, optional) – Specifies whether we perform computationally expensive assertion checks. Computationally cheap assertions will always be checked.

Returns:

A model containing all of the estimated labels (or aliases)

Return type:

Model

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