deltakit.explorer.analysis.get_lambda_fit

deltakit.explorer.analysis.get_lambda_fit(distances: list[int], lep_per_round: list[float], lep_stddev_per_round: list[float]) → ndarray[tuple[Any, ...], dtype[float64]]

Get the best fit line with gradient lambda for plotting purposes.

Accepts the logical error probability (LEP) per round, which may be approximated as LEP / num_rounds (for small LEP), and equally for lep_stddev.

Parameters:

• distances (List[int]) – The distances of the code.

• lep_per_round (List[float]) – The logical error probabilities per round.

• lep_stddev_per_round (List[float]) – The standard deviation of the logical error probabilities per round.

Returns:

The best fit line of log(lep_per_round) vs distance, from which the gradient is lambda.

Return type:

npt.NDArray

Examples

Fit exponential curve given logical error probability for 5, 7, and 9 rounds of QEC experiment:

lep_per_round_fit = Analysis.get_lambda_fit(
    distances=[5, 7, 9],
    lep_per_round=[1.992e-04, 4.314e-05, 7.556e-06],
    lep_stddev_per_round=[1.2e-05, 9.3e-06, 3.9e-06],
)