deltakit.decode.noise_sources.OptionedStim#
- class deltakit.decode.noise_sources.OptionedStim(after_clifford_depolarisation: float = 0.0, before_round_data_depolarisation: float = 0.0, before_measure_flip_probability: float = 0.0, after_reset_flip_probability: float = 0.0)#
Bases:
StimNoiseA class with the ability to manipulate deltakit_stim circuits with after clifford gate depolarisation, before measure flip probability and after reset flip probability. For more information on these noise profiles see: https://github.com/quantumlib/Stim/blob/main/doc/ python_api_reference_vDev.md#stim.Circuit.generated
- Parameters:
after_clifford_depolarisation (float, optional) – Rate at which to depolarize after Clifford gates, by default 0.0.
before_round_data_depolarisation (float, optional) – Rate at which to depolarize before a QEC round, by default 0.0.
before_measure_flip_probability (float, optional) – Rate at which to flip measurement results, by default 0.0.
after_reset_flip_probability (float, optional) – Rate at which to flip reset qubits, by default 0.0.
Methods#
Return the equivalent exhaustive model for this noise model. |
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Given some representation of a code, return a generator of batches of errors for that code. |
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Given some representation of a code, return num_splits number of batch generators of errors for that code and the respective sizes for those generators. |
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Given some representation of a code, return a generator of errors for that code. |
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Return the values of data that characterises this noise model. |
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Return a numpy random number generator, using the member data seed. |
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Expresses the independent error distribution as a statistical mixture of other error distributions. |
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Apply noise to a deltakit_stim circuit |
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Given some representation of a code, return num_splits number of generators of errors for that code and the respective sizes for those generators. |