supermarq.qcvv.ssb

Tooling for Symmetric Stabilizer Benchmarking. See https://arxiv.org/pdf/2407.20184 for more details.

Classes

SSB	Symmetric Stabilizer Benchmarking. A benchmarking algorithm for determining the CZ fidelity
SSBResults	Results from an SSB experiment.

Module Contents

class supermarq.qcvv.ssb.SSB(num_circuits: int, cycle_depths: collections.abc.Iterable[int], *, random_seed: int | numpy.random.Generator | None = None, _samples: list[supermarq.qcvv.base_experiment.Sample] | None = None, **kwargs: str)

Bases: supermarq.qcvv.base_experiment.QCVVExperiment[SSBResults]

Symmetric Stabilizer Benchmarking. A benchmarking algorithm for determining the CZ fidelity of a device. Specifically designed for neutral atom devices where CZ-gates mediated by Rydberg interactions are the native entangling gate.

See: https://arxiv.org/abs/2407.20184

class supermarq.qcvv.ssb.SSBResults

Bases: supermarq.qcvv.base_experiment.QCVVResults

Results from an SSB experiment.

plot_results(filename: str | None = None) → matplotlib.pyplot.Figure

Plot the experiment data and the corresponding fits. The shaded upper and lower limits of the shaded region indicate the fits at +/- 1 standard deviation in all fitted parameters.

Parameters:

filename – Optional argument providing a filename to save the plots to. Defaults to None, indicating not to save the plot.

Returns:

A single matplotlib figure with the experimental data and corresponding fits.

Raises:

RuntimeError – If there is no data stored.

property cz_fidelity_estimate: float

Estimated CZ fidelity.

property cz_fidelity_estimate_std: float

Standard deviation for the CZ fidelity estimate.