pygsti.tools.errgenproptools.bch_numerical

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pygsti.tools.errgenproptools.bch_numerical#

bch_numerical(propagated_errorgen_layers, error_propagator, bch_order=1)#

Iteratively compute effective error generator layer produced by applying the BCH approximation to the list of input error generator matrices. Note this is primarily intended as part of testing and validation infrastructure.

Parameters:

  • propagated_errorgen_layers (list of numpy.ndarrays) – List of the error generator layers to combine using the BCH approximation (in circuit ordering)

  • error_propagator (ErrorGeneratorPropagator) – An ErrorGeneratorPropagator instance to use as part of the BCH calculation.

  • bch_order (int, optional (default 1)) – Order of the BCH approximation to apply (up to 5 is supported currently).

Returns:

A dense numpy array corresponding to the result of the iterative application of the BCH approximation.

Return type:

numpy.ndarray

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