approximate_noise_model#

approximate_noise_model(model, *, operator_string=None, operator_dict=None, operator_list=None)[source]#

Replace all noises in a noise model with ones approximated by a mixture of operators (channels).

Parameters:

  • model (NoiseModel) – the noise model to be approximated. All noises in the model must be 1- or 2-qubit noises.

  • operator_string (string) – a name for a pre-made set of building blocks for the output channel (Default: None). Possible values are 'pauli', 'reset', 'clifford'.

  • operator_dict (dict) – a dictionary whose values are the building blocks for the output channel (Default: None). E.g. {“x”: XGate(), “y”: YGate()}, keys “x” and “y” are not used in transformation.

  • operator_list (list) – list of building block operators for the output channel (Default: None). E.g. [XGate(), YGate()]

Returns:

the approximate noise model.

Return type:

NoiseModel

Raises:

  • NoiseError – if any invalid argument is specified or approximation failed.

  • MissingOptionalLibraryError – if cvxpy is not installed.

Note

The operator input precedence is: list < dict < string. If a string is given, dict is overwritten; if a dict is given, list is overwritten. The string supports only 1- or 2-qubit errors and its possible values are 'pauli', 'reset', 'clifford'. The 'clifford' does not support 2-qubit errors.