pygsti.algorithms.grammatrix
Utility functions related to Gram matrix construction.
Module Contents
Functions
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Compute a maximal set of basis circuits for a Gram matrix. |
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Compute the rank and singular values of a maximal Gram matrix. |
- pygsti.algorithms.grammatrix.max_gram_basis(op_labels, dataset, max_length=0)
Compute a maximal set of basis circuits for a Gram matrix.
That is, a maximal set of strings {S_i} such that the gate strings { S_i S_j } are all present in dataset. If max_length > 0, then restrict len(S_i) <= max_length.
Parameters
- op_labelslist or tuple
the operation labels to use in Gram matrix basis strings
- datasetDataSet
the dataset to use when constructing the Gram matrix
- max_lengthint, optional
the maximum string length considered for Gram matrix basis elements. Defaults to 0 (no limit).
Returns
- list of tuples
where each tuple contains operation labels and specifies a single circuit.
- pygsti.algorithms.grammatrix.max_gram_rank_and_eigenvalues(dataset, target_model, max_basis_string_length=10, fixed_lists=None)
Compute the rank and singular values of a maximal Gram matrix.
That is, compute the rank and singular values of the Gram matrix computed using the basis: max_gram_basis(dataset.gate_labels(), dataset, max_basis_string_length).
Parameters
- datasetDataSet
the dataset to use when constructing the Gram matrix
- target_modelModel
A model used to make sense of circuits and for the construction of a theoretical gram matrix and spectrum.
- max_basis_string_lengthint, optional
the maximum string length considered for Gram matrix basis elements. Defaults to 10.
- fixed_lists(prep_fiducials, effect_fiducials), optional
2-tuple of
Circuitlists, specifying the preparation and measurement fiducials to use when constructing the Gram matrix, and thereby bypassing the search for such lists.
Returns
rank : integer singularvalues : numpy array targetsingularvalues : numpy array