iqm.benchmarks.compressive_gst.gst_analysis.generate_unit_rank_gate_results

iqm.benchmarks.compressive_gst.gst_analysis.generate_unit_rank_gate_results#

iqm.benchmarks.compressive_gst.gst_analysis.generate_unit_rank_gate_results(dataset: Dataset, qubit_layout: List[int], df_g: DataFrame, X_opt: ndarray, K_target: ndarray) → Tuple[DataFrame, DataFrame, Figure, Figure]#

Produces all result tables for Kraus rank 1 estimates and turns them into figures.

This includes parameters of the Hamiltonian generators in the Pauli basis for all gates, as well as the usual performance metrics (Fidelities and Diamond distances). If bootstrapping data is available, error bars will also be generated.

Parameters:

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

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

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

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

  • K_target (ndarray) – 4D numpy array The Kraus operators of all target gates, used to compute distance measures.

Returns:

Pandas DataFrame

The dataframe with properly formated results of standard gate errors

df_g_rotation Pandas DataFrame

A dataframe containing Hamiltonian (rotation) parameters

fig_g: Figure

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

fig_rotation: Figure

A table in Figure format of gate Hamiltonian parameters

Return type:

df_g_final