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_model: list[ndarray], 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) – A 2D array of measurement outcomes for sequences in J; Each column contains the outcome probabilities for a fixed sequence

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

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

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

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

Returns:

  • X_opt_pp, array of optimized gate tensors in Pauli basis

  • E_opt_pp, optimized POVM elements in Pauli basis

  • rho_opt_pp, optimized initial state in Pauli basis

  • df_g.values, array of gate quality measures

  • df_o.values, array of SPAM and other quality measures

  • Whether the optimization successfully converged below expected least-squares error.

Return type:

tuple[ndarray, ndarray, ndarray, ndarray, ndarray, bool]

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