supermarq.qcvv.su2

Tooling for SU(2) benchmarking.

Classes

`SU2`	SU2 benchmarking experiment.
`SU2Results`	Data structure for the SU2 experiment results.

Functions

decay(→ float)

The fitting function used for SU2 benchmarking.

Module Contents

class supermarq.qcvv.su2.SU2(num_circuits: int, cycle_depths: collections.abc.Iterable[int], two_qubit_gate: cirq.Gate = cirq.CZ, *, qubits: collections.abc.Sequence[cirq.Qid] | None = None, 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[SU2Results]

SU2 benchmarking experiment.

SU2 benchmarking extracts the fidelity of a given two qubit gate, even in the presence of additional single qubit errors. The method works by sampling circuits of the form

0: ──│─Rr───Q───X───Q──│─ ^{n} ... ─│─Rr───X─│─ ^{N-n} ... ──Rf───M───
     │      │       │  │            │        │                    │
1: ──│─Rr───Q───X───Q──│─      ... ─│─Rr───X─│─        ... ──Rf───M───

Where each Rr gate is a randomly chosen \(SU(2)\) rotation and the Rf gates are single qubit \(SU(2)\) rotations that in the absence of noise invert the preceding circuit so that the final qubit state should be 00.

An exponential fit decay is then fitted to the observed 00 state probability as it decays with the number of two qubit gates included. Note that all circuits contain a fixed number of single qubit gates, so that the contribution for single qubit noise is constant.

See Fig. 3 of https://www.nature.com/articles/s41586-023-06481-y#Fig3 for further details.

two_qubit_gate: The two qubit gate to be benchmarked

class supermarq.qcvv.su2.SU2Results

Bases: supermarq.qcvv.base_experiment.QCVVResults

Data structure for the SU2 experiment results.

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

Plot the results of the experiment.

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 containing the relevant plots of the results data.
Raises:: RuntimeError – If there is no data stored.

property single_qubit_noise: float: Returns: Estimated single qubit noise.

property single_qubit_noise_std: float: Returns: Standard deviation of estimated single qubit noise.

property two_qubit_gate_error: float: Returns: The two qubit gate error. Equal to one minus the fidelity.

property two_qubit_gate_error_std: float: Returns: The two qubit gate error standard deviation. Equal to standard deviation of the fidelity.

property two_qubit_gate_fidelity: float: Returns: Estimated two qubit gate fidelity.

property two_qubit_gate_fidelity_std: float: Returns: Standard deviation of estimated two qubit gate fidelity.

supermarq.qcvv.su2.decay(x: float, single_qubit_noise: float, two_qubit_gate_fidelity: float) → float

The fitting function used for SU2 benchmarking.

Parameters:

x – The number of two qubit gates.
single_qubit_noise – The single qubit noise parameter.
two_qubit_gate_fidelity – The two qubit gate fidelity parameter.

Returns:

The expected probability of measuring the |00> state.