pygsti.extras.idletomography.idtcore

Core Idle Tomography routines

Functions

affine_jac_element(prep, error, meas, outcome)	Computes the Jacobian matrix element for a Affine error: how the probability of outcome changes with respect to the rate of error when preparing state prep and measuring in basis meas.
affine_jac_obs_element(prep, error, observable)	Computes the Jacobian matrix element for a Affine error: how the expectation value of observable changes with respect to the rate of error when preparing state prep.
compute_observed_diffbasis_err_rate(dataset, ...)	Extract the observed error rate from a series of experiments which prepares and measures in different Pauli basis and tracks the expectation value of a particular observable.
compute_observed_samebasis_err_rate(dataset, ...)	Extract the observed error rate from a series of experiments which prepares and measures in the same Pauli basis and tracks a particular outcome.
determine_paulidicts(model)	Intelligently determine preparation and measurement Pauli basis dictionaries from a Model.
do_idle_tomography(nqubits, dataset, ...[, ...])	Analyze dataset using the idle tomography protocol to characterize idle_string.
fidpairs_to_pauli_fidpairs(fidpairs_list, ...)	Translate GatesString-type fiducial pairs to NQPauliState-type "Pauli fiducial pairs" using pauli_basis_dicts.
hamiltonian_jac_element(prep, error, observable)	Computes the Jacobian matrix element for a Hamiltonian error: how the expectation value of observable in state prep changes due to Hamiltonian error.
idle_tomography_fidpairs(nqubits[, ...])	Construct a list of Pauli-basis fiducial pairs for idle tomography.
make_idle_tomography_list(nqubits, ...[, ...])	Construct the list of experiments needed to perform idle tomography.
make_idle_tomography_lists(nqubits, ...[, ...])	Construct lists of experiments, one for each maximum-length value, needed to perform idle tomography.
preferred_signs_from_paulidict(pauli_basis_dict)	Infers what the preferred basis signs are based on the length of gate-name strings in pauli_basis_dict (shorter strings are preferred).
stochastic_jac_element(prep, error, meas, ...)	Computes the Jacobian matrix element for a Stochastic error: how the probability of outcome changes with respect to the rate of error when preparing state prep and measuring in basis meas.
stochastic_outcome(prep, error, meas)	Computes the "expected" outcome when the stochastic error error occurs between preparing in prep and measuring in basis meas.