pygsti.algorithmsΒΆ

Gate Set Tomography Algorithms Python Package

Functions

bulk_twirled_deriv(gateset, gatestrings[, ...]) Compute the “Twirled Derivative” of a gatestring, obtained by acting on the standard derivative of a gate string with the twirling superoperator.
contract(gateset, toWhat[, dataset, ...]) Contract a GateSet to a specified space.
do_exlgst(dataset, startGateset, ...[, ...]) Performs Extended Linear-inversion Gate Set Tomography on the dataset.
do_iterative_exlgst(dataset, startGateset, ...) Performs Iterated Extended Linear-inversion Gate Set Tomography on the dataset.
do_iterative_mc2gst(dataset, startGateset, ...) Performs Iterative Minimum Chi^2 Gate Set Tomography on the dataset.
do_iterative_mc2gst_with_model_selection(...) Performs Iterative Minimum Chi^2 Gate Set Tomography on the dataset, and at each iteration tests the current gateset model against gateset models with an increased and/or decreased dimension (model selection).
do_iterative_mlgst(dataset, startGateset, ...) Performs Iterative Maximum Liklihood Estimation Gate Set Tomography on the dataset.
do_lgst(dataset, specs[, targetGateset, ...]) Performs Linear-inversion Gate Set Tomography on the dataset.
do_mc2gst(dataset, startGateset, ...[, ...]) Performs Least-Squares Gate Set Tomography on the dataset.
do_mc2gst_with_model_selection(dataset, ...) Performs Least-Squares Gate Set Tomography on the dataset.
do_mlgst(dataset, startGateset, gateStringsToUse) Performs Maximum Likelihood Estimation Gate Set Tomography on the dataset.
find_closest_unitary_gatemx(gateMx) Get the closest gate matrix (by maximizing fidelity) to gateMx that describes a unitary quantum gate.
find_sufficient_fiducial_pairs(...[, ...]) Still in experimental stages.
get_max_gram_basis(gateLabels, dataset[, ...]) Compute a maximal set of gate strings that can be used as a basis for a Gram matrix.
gram_rank_and_evals(dataset, specs[, ...]) Returns the rank and singular values of the Gram matrix for a dataset.
make_meas_mxs(gs, prepMeasList) Makes a list of matrices, where each matrix corresponds to a single measurement effect in the gate set, and the column of each matrix is the transpose of the measurement effect acting on a fiducial.
make_prep_mxs(gs, prepFidList) Makes a list of matrices, where each matrix corresponds to a single preparation operation in the gate set, and the column of each matrix is a fiducial acting on that state preparation.
max_gram_rank_and_evals(dataset[, ...]) Compute the rank and singular values of a maximal Gram matrix,that is, the Gram matrix using a basis computed by: get_max_gram_basis(dataset.get_gate_labels(), dataset, maxBasisStringLength).
optimize_gauge(gateset, toGetTo[, maxiter, ...]) Optimize the gauge of a GateSet using some ‘goodness’ function.
optimize_integer_fiducials_slack(gateset, ...) Find a locally optimal subset of the fiducials in fidList.
optimize_integer_germs_slack(gateset, germsList) Find a locally optimal subset of the germs in germsList.
test_fiducial_list(gateset, fidList, prepOrMeas) Tests a prep or measure fiducial list for informational completeness.
test_germ_list_finitel(gateset, germsToTest, L) Test whether a set of germs is able to amplify all of the gateset’s non-gauge parameters.
test_germ_list_infl(gateset, germsToTest[, ...]) Test whether a set of germs is able to amplify all of the gateset’s non-gauge parameters.
twirled_deriv(gateset, gatestring[, eps]) Compute the “Twirled Derivative” of a gatestring, obtained by acting on the standard derivative of a gate string with the twirling superoperator.
write_fixed_hamming_weight_code(n, k) This is an auxiliary function (probably to be deprecated soon) for the fixedNum mode of optimize_integer_fiducials_slack.
xor(*args) Implements logical xor function for arbitrary number of inputs.