View source on GitHub
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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 |
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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).
Args | |
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sampled_df
|
The DataFrame of sampled two-qubit probability distributions returned
from sample_2q_xeb_circuits.
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parameterized_circuits
|
The circuits corresponding to those sampled in sampled_df,
but with some gates parameterized, likely by using parameterize_circuit.
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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.
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fatol
|
The fatol argument for Nelder-Mead. This is the absolute error for convergence
in the function evaluation.
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pool
|
An optional multiprocessing pool to execute pair optimization in parallel. Each optimization (and the simulations therein) runs serially. |