iqm.benchmarks.compressive_gst.mgst.reporting.reporting.quick_report

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iqm.benchmarks.compressive_gst.mgst.reporting.reporting.quick_report#

iqm.benchmarks.compressive_gst.mgst.reporting.reporting.quick_report(X: ndarray, E: ndarray, rho: ndarray, J: ndarray, y: ndarray, X_t: ndarray, E_t: ndarray, rho_t: ndarray, gate_labels: dict[int, str] | None = None, weights: list[float] | None = None) → tuple[DataFrame, DataFrame]#

Generation of pandas dataframes with gate and SPAM quality measures.

The quick report is intended to check on a GST estimate with fast to compute measures (no diamond distance) to get a first picture and check whether mGST and the gauge optimization produce meaningful results.

Parameters:

  • X (ndarray) – Gate set

  • E (ndarray) – POVM

  • rho (ndarray) – Initial state

  • J (ndarray) – 2D array where each row contains the gate indices of a gate sequence

  • y (ndarray) – 2D array of measurement outcomes for sequences in J; Each column contains the outcome probabilities for a fixed sequence

  • X_t (ndarray) – Target gate set

  • E_t (ndarray) – Target POVM

  • rho_t (ndarray) – Target initial state

  • gate_labels (dict[int, str] | None) – A list of names for the gates in X

  • weights (list[float] | None) – A list of weights used for gauge optimization

Returns:

DataFrame of gate quality measures df_o: DataFrame of all other quality/error measures

Return type:

df_g

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