cirq.experiments.xeb_fitting.characterize_phased_fsim_parameters_with_xeb_by_pair

Run a classical optimization to fit phased fsim parameters to experimental data, and

cirq.experiments.xeb_fitting.characterize_phased_fsim_parameters_with_xeb_by_pair(
    sampled_df: pd.DataFrame,
    parameterized_circuits: List['cirq.Circuit'],
    cycle_depths: Sequence[int],
    options: cirq.experiments.xeb_fitting.XEBCharacterizationOptions,
    initial_simplex_step_size: float = 0.1,
    xatol: float = 0.001,
    fatol: float = 0.001,
    pool: Optional['multiprocessing.pool.Pool'] = None
) -> cirq.experiments.xeb_fitting.XEBCharacterizationResult

Used in the notebooks

Used in the tutorials

thereby characterize PhasedFSim-like gates grouped by pairs.

This is appropriate if you have run parallel XEB on multiple pairs of qubits.

The optimization is done per-pair. If you have the same pair in e.g. two different layers the characterization optimization will lump the data together. This is in contrast with the benchmarking functionality, which will always index on (layer_i, pair_i, pair).

sampled_df The DataFrame of sampled two-qubit probability distributions returned from sample_2q_xeb_circuits.
parameterized_circuits The circuits corresponding to those sampled in sampled_df, but with some gates parameterized, likely by using parameterize_circuit.
cycle_depths The depths at which circuits were truncated.
options A set of options that controls the classical optimization loop for characterizing the parameterized gates.
initial_simplex_step_size Set the size of the initial simplex for Nelder-Mead.
xatol The xatol argument for Nelder-Mead. This is the absolute error for convergence in the parameters.
fatol The fatol argument for Nelder-Mead. This is the absolute error for convergence in the function evaluation.
pool An optional multiprocessing pool to execute pair optimization in parallel. Each optimization (and the simulations therein) runs serially.