iqm.benchmarks.compressive_gst.mgst.reporting.reporting.generate_rotation_param_results

iqm.benchmarks.compressive_gst.mgst.reporting.reporting.generate_rotation_param_results#

iqm.benchmarks.compressive_gst.mgst.reporting.reporting.generate_rotation_param_results(dataset: Dataset, qubit_layout: list[int], X_opt: ndarray, K_target: ndarray, X_array: ndarray | None = None, E_array: ndarray | None = None, rho_array: ndarray | None = None) → tuple[DataFrame, dict]#

Produces result tables and data for Kraus rank 1 estimates.

This includes parameters of the Hamiltonian generators in the Pauli basis for all gates. 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

  • 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.

  • X_array (ndarray | None) – ndarray, optional Array of bootstrap gate estimates, used for error bars

  • E_array (ndarray | None) – ndarray, optional Array of bootstrap POVM estimates, used for error bars

  • rho_array (ndarray | None) – ndarray, optional Array of bootstrap state estimates, used for error bars

Returns:

  • df_g_rotation: Pandas DataFrame containing Hamiltonian (rotation) parameters with formatted values

  • hamiltonian_params: Dictionary with raw values and uncertainty bounds if bootstrapping was used

Return type:

Tuple[DataFrame, dict]

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