iqm.benchmarks.compressive_gst.gst_analysis.process_layout

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iqm.benchmarks.compressive_gst.gst_analysis.process_layout#

iqm.benchmarks.compressive_gst.gst_analysis.process_layout(args: Tuple[Dataset, List[int], int]) → Tuple[List[int], dict[str, Any], List[BenchmarkObservation], DataFrame, DataFrame, DataFrame]#

Process a single qubit layout for Gate Set Tomography analysis.

This function performs the full GST workflow for a single qubit layout: 1. Convert counts to mGST format 2. Run mGST reconstruction 3. Perform gauge optimization 4. Generate reports and metrics 5. Run bootstrap analysis if configured 6. Format results into dataframes and observations

Parameters:

args (Tuple[Dataset, List[int], int]) – Tuple containing: dataset: xr.Dataset, qubit_layout: List[int], pdim: int

Returns:

List[int]

The input qubit layout being processed

results_dict: dict[str, Any]

Dictionary containing all raw and processed results

layout_observations: List[BenchmarkObservation]

List of benchmark observations for this layout

df_g_final: DataFrame

DataFrame containing gate metrics (fidelity, diamond distance, etc.)

df_o_final: DataFrame

DataFrame containing non-gate metrics (SPAM errors, fit quality)

df_g_evals: DataFrame

DataFrame containing Choi matrix eigenvalues (for rank > 1)

Return type:

qubit_layout

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