pygsti.optimize.wildcardopt
Wildcard budget fitting routines
Module Contents
Functions
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Uses repeated Nelder-Mead to optimize the wildcard budget. |
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Uses CVXPY to optimize the wildcard budget. Includes only per-circuit constraints. |
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Uses a barrier method (for convex optimization) to optimize the wildcard budget. |
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- pygsti.optimize.wildcardopt.optimize_wildcard_budget_neldermead(budget, L1weights, wildcard_objfn, two_dlogl_threshold, redbox_threshold, printer, smart_init=True, max_outer_iters=10, initial_eta=10.0)
Uses repeated Nelder-Mead to optimize the wildcard budget. Includes both aggregate and per-circuit constraints.
- pygsti.optimize.wildcardopt.optimize_wildcard_budget_percircuit_only_cvxpy(budget, L1weights, objfn, redbox_threshold, printer)
Uses CVXPY to optimize the wildcard budget. Includes only per-circuit constraints.
- pygsti.optimize.wildcardopt.optimize_wildcard_bisect_alpha(budget, objfn, two_dlogl_threshold, redbox_threshold, printer, guess=0.1, tol=0.001)
- pygsti.optimize.wildcardopt.optimize_wildcard_budget_barrier(budget, L1weights, objfn, two_dlogl_threshold, redbox_threshold, printer, tol=1e-07, max_iters=50, num_steps=3, save_debugplot_data=False)
Uses a barrier method (for convex optimization) to optimize the wildcard budget. Includes both aggregate and per-circuit constraints.
- pygsti.optimize.wildcardopt.NewtonSolve(initial_x, fn, fn_with_derivs=None, dx_tol=1e-06, max_iters=20, printer=None, lmbda=0.0)