iqm.benchmarks.compressive_gst.gst_analysis.generate_gate_results

iqm.benchmarks.compressive_gst.gst_analysis.generate_gate_results#

iqm.benchmarks.compressive_gst.gst_analysis.generate_gate_results(dataset: Dataset, qubit_layout: list[int], df_g: DataFrame, X_opt: ndarray, E_opt: ndarray, rho_opt: ndarray, bootstrap_results: None | tuple[Any, Any, Any, Any, Any] = None, max_evals: int = 6) → tuple[DataFrame, DataFrame]#

Produces all result tables for arbitrary Kraus rank estimates.

Parameters:

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

  • qubit_layout (list[int]) – The list of qubits for the current GST experiment

  • df_g (DataFrame) – The dataframe with properly formated results

  • X_opt (ndarray) – The gate set after gauge optimization

  • E_opt (ndarray) – An array containg all the POVM elements as matrices after gauge optimization

  • rho_opt (ndarray) – The density matrix after gauge optmization

  • bootstrap_results (None | tuple[Any, Any, Any, Any, Any]) – If provided, contains the results of the bootstrap analysis.

  • max_evals (int) – The maximum number of eigenvalues of the Choi matrices which are returned.

Returns:

The dataframe with properly formated results of standard gate errors df_g_evals_final:

A dataframe containing eigenvalues of the Choi matrices for all gates

Return type:

df_g_final

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