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, max_evals: int = 6) → Tuple[DataFrame, DataFrame, Figure, Figure]#

Produces all result tables for arbitrary Kraus rank estimates and turns them into figures.

Parameters:

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

  • X_opt (ndarray) – 3D numpy array The gate set after gauge optimization

  • E_opt (ndarray) – 3D numpy array An array containg all the POVM elements as matrices after gauge optimization

  • rho_opt (ndarray) – 2D numpy array The density matrix after gauge optmization

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

  • dataset (Dataset) –

  • qubit_layout (List[int]) –

Returns:

Pandas DataFrame

The dataframe with properly formated results of standard gate errors

df_g_evals_final Pandas DataFrame

A dataframe containing eigenvalues of the Choi matrices for all gates

fig_g: Figure

A table in Figure format of gate results (fidelities etc.)

fig_choi: Figure

A table in Figure format of eigenvalues of the Choi matrices of all gates

Return type:

df_g_final