Update germ selection and FPR to better work with implicit models #668
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…be used in germ selection. And partially in fiducial selection.
Replaces these calls with more general _copy_to_static_explicitop_model
calls, which call model.set_all_parameterizations('static') when
the model is an explicit op model but construct such a model manually
when an implicit model is given. This allows implicit target models
to be given to FPR routines such as
pygsti.alg.find_sufficient_fiducial_pairs_per_germ.
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@enielse can you add one test each to test_fiducialpairreduction.py and test_germselection.py (both in test/unit/algorithms/)? A natural test would be to make an equivalent ImplicitOpModel from an ExplicitOpModel and see that FPR and germ selection return the same results for the ImplicitOpModel. I'd welcome the addition of utility functions for these classes that convert between one another. |
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Adds logic to allow implicit models to be used for germ selection and fiducial pair reduction. This basically just adds case logic to interpret an
ImplicitOpModel'sprep_blks['layers'],povm_blks['layers'], andoperation_blks['layers']elements as the state prep, POVM, and gate operations of that model (parallel to the.preps,.povms, and.operationsof anExplicitOpModel). I don't think this necessarily handles all the edge cases, but I've found it to work fine when usingLocalNoiseModelobjects when you also specify thenum_nongauge_paramswhen performing germ selection.