pygsti.extras.idletomography.idtresults
Idle Tomography results object
Module Contents
Classes
A container for idle tomography results: intrinsic and observable errors, |
- class pygsti.extras.idletomography.idtresults.IdleTomographyResults(dataset, max_lengths, max_error_weight, fit_order, pauli_dicts, idle_str, error_list, intrinsic_rates, pauli_fidpairs, observed_rate_infos)
Bases:
objectA container for idle tomography results: intrinsic and observable errors, along with supporting information.
Create a IdleTomographyResults object.
Parameters
- datasetDataSet
The dataset that was analyzed, containing the observed counts.
- max_lengthslist
The series of maximum lengths used.
- max_error_weightint
The maximum error weight.
- fit_orderint
The order of the polynomial fits used.
- pauli_dictstuple
A 2-tuple of (prepDict,measDict) Pauli basis dictionaries.
- idle_strCircuit
The idle operation that was characterized.
- error_listlist
A list of
NQPauliOpobjects describing the errors Paulis considered for each intrinsic-error type.- intrinsic_ratesdict
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_fidpairsdict
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
NQPauliStateobjects.- observed_rate_infosdict
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) orNQPauliOp(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]