pygsti.extras.idletomography.idttools.predicted_intrinsic_rates

pygsti.extras.idletomography.idttools.predicted_intrinsic_rates#

predicted_intrinsic_rates(nqubits, maxweight, model, hamiltonian=True, stochastic=True, affine=True)#

Get the exact intrinsic rates that would be produced by simulating model (for comparison with idle tomography results).

Parameters:

  • nqubits (int) – The number of qubits.

  • maxweight (int, optional) – The maximum weight of errors to consider.

  • model (CloudNoiseModel) – The model to extract intrinsic error rates from.

  • hamiltonian (bool, optional) – Whether model includes Hamiltonian, Stochastic, and/or Affine errors (e.g. if the model was built with “H+S” parameterization, then only hamiltonian and stochastic should be set to True).

  • stochastic (bool, optional) – Whether model includes Hamiltonian, Stochastic, and/or Affine errors (e.g. if the model was built with “H+S” parameterization, then only hamiltonian and stochastic should be set to True).

  • affine (bool, optional) – Whether model includes Hamiltonian, Stochastic, and/or Affine errors (e.g. if the model was built with “H+S” parameterization, then only hamiltonian and stochastic should be set to True).

Returns:

ham_intrinsic_rates, sto_intrinsic_rates, aff_intrinsic_rates – Arrays of intrinsic rates. None if corresponding hamiltonian, stochastic or affine is set to False.

Return type:

numpy.ndarray