What additional options are available in PEA?#

execute_with_pea() exposes several optional arguments beyond the required circuit, executor, scale_factors, noise_model, epsilon, and extrapolation_method. This page describes each one.

Controlling the sampling budget#

The number of probabilistically sampled circuits per scale factor controls the statistical uncertainty of the PEA estimate. By default, this is derived from the precision argument:

\[N_\text{samples} = \left(\frac{\gamma}{\texttt{precision}}\right)^2\]

where \(\gamma\) is the one-norm of the quasi-probability representation (always 1.0 for PEA). A smaller precision value gives more samples and a lower-variance estimate, at the cost of more circuit executions.

mitigated = pea.execute_with_pea(
    circuit,
    executor,
    scale_factors=[1.0, 1.2, 1.6],
    noise_model="local_depolarizing",
    epsilon=0.005,
    extrapolation_method=LinearFactory.extrapolate,
    precision=0.05,   # smaller precision => more samples
)

If you want to fix the number of samples directly, pass num_samples instead. When num_samples is provided, precision is ignored:

mitigated = pea.execute_with_pea(
    ...,
    num_samples=500,
)

Reproducibility with `random_state`#

PEA sampling is stochastic. Pass an integer seed or a numpy.random.RandomState to make results reproducible:

mitigated = pea.execute_with_pea(
    ...,
    random_state=42,
)

Inspecting raw results with `full_output`#

Setting full_output=True returns a tuple (pea_value, pea_data) where pea_data is a dictionary containing the intermediate results from each step:

pea_value, pea_data = pea.execute_with_pea(
    ...,
    full_output=True,
)

pea_data.keys()
# dict_keys(['num_samples', 'precision', 'pea_value',
#            'scaled_expectation_values', 'scaled_sampled_circuits'])

scaled_expectation_values holds the raw executor results for each sampled circuit at each scale factor, and scaled_sampled_circuits holds the corresponding circuits. These are useful for debugging or post-processing.

Forcing re-execution of duplicate circuits#

By default, Mitiq deduplicates circuits before execution (i.e. identical circuits are run once and their results reused). To disable this and force every sampled circuit to be executed independently pass force_run_all=True:

mitigated = pea.execute_with_pea(
    ...,
    force_run_all=True,
)

Observables and executors#

If you pass an observable to execute_with_pea(), the executor is assumed to return a MeasurementResult and Mitiq computes the expectation value of the observable for you. If no observable is provided, the executor must return the expectation value directly.

Supported noise models#

Attention

The only supported noise models are currently "local_depolarizing" and "global_depolarizing". Please open an issue to request additional noise models.