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. |