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, bootstrap_results: None | tuple[Any, Any, Any, Any, Any] = None) → Tuple[DataFrame, DataFrame, dict]#

Produces all result tables for Kraus rank 1 estimates

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.

  • bootstrap_results (None | tuple[Any, Any, Any, Any, Any]) –

Returns:

Pandas DataFrame

The dataframe with properly formated results of standard gate errors

df_g_rotation Pandas DataFrame

A dataframe containing Hamiltonian (rotation) parameters

hamiltonian_params: dict

A dictionary containing the Hamiltonian parameters for each gate in the Pauli basis. The keys are gate labels and the values are dictionaries with the parameters.

Return type:

df_g_final