cirq.experiments.xeb_fitting.characterize_phased_fsim_parameters_with_xeb

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

cirq.experiments.xeb_fitting.characterize_phased_fsim_parameters_with_xeb(
    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,
    verbose: bool = True,
    pool: Optional['multiprocessing.pool.Pool'] = None
) -> cirq.experiments.xeb_fitting.XEBCharacterizationResult

thereby characterize PhasedFSim-like gates.

Args
`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.
`verbose`	Whether to print progress updates.
`pool`	An optional multiprocessing pool to execute circuit simulations in parallel.

cirq.experiments.xeb_fitting.characterize_phased_fsim_parameters_with_xeb

Args