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
Used in the notebooks
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.
|