iqm.benchmarks.compressive_gst.gst_analysis.bootstrap_errors

iqm.benchmarks.compressive_gst.gst_analysis.bootstrap_errors#

iqm.benchmarks.compressive_gst.gst_analysis.bootstrap_errors(dataset: Dataset, y: ndarray, gate_set: list[ndarray], target_gate_set: list[ndarray], identifier: str, parametric: bool = True) → tuple[Any, Any, Any, Any, Any]#

Resamples circuit outcomes a number of times and computes GST estimates for each repetition.

All results are then returned in order to compute bootstrap-error bars for GST estimates. Parametric bootstrapping uses the estimated gate set to create a newly sampled data set. Non-parametric bootstrapping uses the initial dataset and resamples according to the corresp. outcome probabilities. Each bootstrap run is initialized with the estimated gate set in order to save processing time.

Parameters:

  • dataset (Dataset) – A dataset containing counts from the experiment and configurations

  • y (ndarray) – The circuit outcome probabilities as a num_povm x num_circuits array

  • gate_set (list[ndarray]) – The estimated gate set in format [K, X, E, rho]

  • target_gate_set (list[ndarray]) – The target gate set in format [X_target, E_target, rho_target]

  • identifier (str) – The string identifier of the current benchmark

  • parametric (bool) – If set to True, parametric bootstrapping is used, else non-parametric bootstrapping. Default: False

Returns:

Array containing all estimated gate tensors of different bootstrapping repetitions along first axis E_array:

Array containing all estimated POVM tensors of different bootstrapping repetitions along first axis

rho_array:

Array containing all estimated initial states of different bootstrapping repetitions along first axis

df_g_array:

Contains gate quality measures of bootstrapping repetitions

df_o_array:

Contains SPAM and other quality measures of bootstrapping repetitions

Return type:

X_array

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