iqm.benchmarks.compressive_gst.gst_analysis.process_plots

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iqm.benchmarks.compressive_gst.gst_analysis.process_plots#

iqm.benchmarks.compressive_gst.gst_analysis.process_plots(dataset: Dataset, qubit_layout: List[int], results_dict: dict[str, Any], df_g_final: DataFrame, df_o_final: DataFrame, df_g_evals_final: DataFrame) → dict[str, Figure]#

Process and generate all plots for a single qubit layout.

This function creates various visualization plots for gate set tomography results, including gate metrics tables, process matrices, and SPAM (State Preparation And Measurement) matrices in both real and imaginary parts.

Parameters:

  • dataset (Dataset) – xarray Dataset containing experimental data and configuration attributes

  • qubit_layout (List[int]) – List of qubit indices defining the current layout

  • results_dict (dict[str, Any]) – Dictionary containing gauge-optimized gates, POVM elements, and states in both standard and Pauli basis

  • df_g_final (DataFrame) – DataFrame containing gate metrics such as fidelity and diamond distance

  • df_o_final (DataFrame) – DataFrame containing non-gate metrics such as SPAM errors

  • df_g_evals_final (DataFrame) – DataFrame containing Choi matrix eigenvalues (can be empty)

Returns:

Dictionary mapping plot names to matplotlib Figure objects.

Keys follow the pattern “layout_{qubit_layout}_{plot_type}”

Return type:

layout_plots

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