deltakit.decode.noise_sources.ToyNoise#

class deltakit.decode.noise_sources.ToyNoise(p_physical: float)#

A noise model which adds:

Depolarise 1 channel after every one qubit gate, reset gate and measurement gates (with the exception of the MPP gate)
Inverse noise after every two qubit gate and MPP gate. Inverse noise consists of a depolarise 2 channel after every two qubit gate in a gate layer and a depolarise 1 channel after all qubits that are not acted on in the gate layer
Depolarise1 channels have an error probability of p_physical/10 while depolarise2 channels have an error probability of p_physical

Parameters:: p_physical (float) – Single parameter used to define the rate of occurrence of several noise channels.

Methods#

`ToyNoise.as_exhaustive_sequential_model`	Return the equivalent exhaustive model for this noise model.
`ToyNoise.build_batch_error_generator`	Given some representation of a code, return a generator of batches of errors for that code.
`ToyNoise.build_split_batch_error_generators`	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.
`ToyNoise.error_generator`	Given some representation of a code, return a generator of errors for that code.
`ToyNoise.field_values`	Return the values of data that characterises this noise model.
`ToyNoise.get_rng`	Return a numpy random number generator, using the member data seed.
`ToyNoise.importance_sampling_decomposition`	Expresses the independent error distribution as a statistical mixture of other error distributions.
`ToyNoise.permute_stim_circuit`	Apply noise to a lestim circuit
`ToyNoise.split_error_generator`	Given some representation of a code, return num_splits number of generators of errors for that code and the respective sizes for those generators.