pygsti.extras.idletomography.idtresults.IdleTomographyResults

class IdleTomographyResults(dataset, max_lengths, max_error_weight, fit_order, pauli_dicts, idle_str, error_list, intrinsic_rates, pauli_fidpairs, observed_rate_infos)

Bases: object

A container for idle tomography results: intrinsic and observable errors, along with supporting information.

Create a IdleTomographyResults object.

Parameters:

• dataset (DataSet) – The dataset that was analyzed, containing the observed counts.

• max_lengths (list) – The series of maximum lengths used.

• max_error_weight (int) – The maximum error weight.

• fit_order (int) – The order of the polynomial fits used.

• pauli_dicts (tuple) – A 2-tuple of (prepDict,measDict) Pauli basis dictionaries.

• idle_str (Circuit) – The idle operation that was characterized.

• error_list (list) – A list of NQPauliOp objects describing the errors Paulis considered for each intrinsic-error type.

• intrinsic_rates (dict) – A dictionary of the intrinsic rates. Keys are intrinsic-rate-types, i.e. ‘hamiltonian’, ‘stochastic’, or ‘affine’. Values are numpy arrays of length len(error_list).

• pauli_fidpairs (dict) – A dictionary of the pauli-state fiducial pairs. Keys are observed-rate-types, i.e. ‘samebasis’ or ‘diffbasis’, and values are lists of (prep,meas) 2-tuples of NQPauliState objects.

• observed_rate_infos (dict) – A dictionary of observed-rate information dictionaries. Keys are observed-rate-types, i.e. ‘samebasis’ or ‘diffbasis’, and values are further dictionaries indexed by fiducial pair (i.e. an element of pauli_fidpairs, then either a NQOutcome (for the “samebasis” case) or NQPauliOp (for “diffbasis”) case. After these two indexes, the value is another dictionary of information about the observeable rate so defined. So, to get to an actual “info dict” you need to do something like: observed_rate_infos[typ][fidpair][obsORoutcome]

Methods

__init__(dataset, max_lengths, ...)

Create a IdleTomographyResults object.