iqm.benchmarks.compressive_gst.gst_analysis.process_bootstrap_samples

iqm.benchmarks.compressive_gst.gst_analysis.process_bootstrap_samples#

iqm.benchmarks.compressive_gst.gst_analysis.process_bootstrap_samples(y_sampled: ndarray, attrs: dict[str, Any], init: list[ndarray], target_mdl: Model, identifier: str) → tuple[ndarray, ndarray, ndarray, ndarray, ndarray, bool]#

Process a single bootstrap sample for Gate Set Tomography.

This function performs a GST analysis on a sampled dataset, applies gauge optimization, and generates result reports.

Parameters:

  • y_sampled (ndarray) – ndarray A 2D array of measurement outcomes for sequences in J; Each column contains the outcome probabilities for a fixed sequence

  • attrs (dict[str, Any]) – dict[str, Any] Dictionary containing configuration parameters for the GST algorithm

  • init (list[ndarray]) – list[ndarray] Initial values for the gate set optimization [K, E, rho]

  • target_mdl (Model) – Model The target gate set model

  • identifier (str) – str String identifier for the current qubit layout

Returns:

ndarray

Array of optimized gate tensors in Pauli basis

E_opt_pp: ndarray

Optimized POVM elements in Pauli basis

rho_opt_pp: ndarray

Optimized initial state in Pauli basis

df_g.values: ndarray

Array of gate quality measures

df_o.values: ndarray

Array of SPAM and other quality measures

opt_success: bool

Whether the optimization successfully converged below expected least-squares error

Return type:

X_opt_pp