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ZneOptions

class ZneOptions(*args, **kwargs)

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Zero noise extrapolation mitigation options. This is only used by the V2 Estimator.

Note

Any V2 estimator is guaranteed to return data fields called evs and stds that report the desired expectation value estimates and errors, respectively. When ZNE options are enabled in the runtime estimator, additional data is returned.

In particular, suppose an input pub has observable array shape obs_shape and parameter values shape par_shape, with corresponding pub shape shape=np.broadcast_shapes(obs_shape, par_shape). Then the corresponding pub result will additionally contain:

  1. pub_result.data.evs_extrapolated and pub_result.data.stds_extrapolated,

    both with shape (*shape, num_extrapolators, num_evaluation_points), where num_extrapolators is the length of the list of options.resilience.zne.extrapolators, and num_evaluation_points is the length of the list options.resilience.extrapolated_noise_factors. These values provide evaluations of every extrapolator at every specified noise extrapolation value.

  2. pub_result.data.evs_noise_factors, pub_result.data.stds_noise_factors, and ensemble_stds_noise_factors all have shape (*shape, num_noise_factors) where num_noise_factors is the length of options.resilience.zne.noise_factors. These values provide evaluations of the best-fit model at each of the noise amplifications. In the case of no twirling, both *stds* arrays will be equal, otherwise, stds_noise_factors is derived from the spread over twirling samples, whereas ensemble_stds_noise_factors assumes only shot noise and no drift.

Technical note: for single observables with multiple basis terms it might turn out that multiple extrapolation methods are used in the same expectation value, for example, XX gets linearly extrapolated but XY gets exponentially extrapolated in the observable {"XX": 0.5, "XY": 0.5}. Let’s call this a hetergeneous fit. The data from (2) is evaluated from heterogeneous fits by selecting the best fit for every individual distinct term, whereas data from (1) is evaluated from forced homogenous fits, one for each provided extrapolator. If your work requires a nuanced distinction in this regard, we presently recommend that you use single-term observables in addition to your multi-term observables.

Parameters

  • amplifier –

    Which technique to use for amplifying noise. One of:

    • ”gate_folding” (default) uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are chosen randomly.

    • ”gate_folding_front” uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are selected from the front of the topologically ordered DAG circuit.

    • ”gate_folding_back” uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are selected from the back of the topologically ordered DAG circuit.

    • ”pea” uses a technique called Probabilistic Error Amplification (PEA(opens in a new tab)) to amplify noise.

      When this option is selected, gate twirling will always be used whether or not it has been enabled in the options.

      In this technique, the twirled noise model of each each unique layer of entangling gates in your ISA circuits is learned beforehand, see LayerNoiseLearningOptions for relevant learning options. Once complete, your circuits are executed at each noise factor, where every entangling layer of your circuits is amplified by probabilistically injecting single-qubit noise proportional to the corresponding learned noise model.

  • noise_factors – Noise factors to use for noise amplification. Default: (1, 1.5, 2) for PEA, and (1, 3, 5) otherwise.

  • extrapolated_noise_factors – Noise factors to evaluate the fit extrapolation models at. If unset, this will default to [0, *noise_factors]. This option does not affect execution or model fitting in any way, it only determines the points at which the extrapolator``s are evaluated to be returned in the data fields called ``evs_extrapolated and stds_extrapolated.

  • extrapolator –

    Extrapolator(s) to try (in order) for extrapolating to zero noise. One or more of:

    • ”linear”
    • ”exponential”
    • ”double_exponential”
    • ”polynomial_degree_(1 <= k <= 7)”

    Default: (“exponential”, “linear”).

Attributes

amplifier

Type: UnsetType | Literal['gate_folding', 'gate_folding_front', 'gate_folding_back', 'pea']

Default value: Unset

extrapolated_noise_factors

Type: UnsetType | Sequence[float]

Default value: Unset

extrapolator

Type: UnsetType | Literal['linear', 'exponential', 'double_exponential', 'polynomial_degree_1', 'polynomial_degree_2', 'polynomial_degree_3', 'polynomial_degree_4', 'polynomial_degree_5', 'polynomial_degree_6', 'polynomial_degree_7'] | Sequence[Literal['linear', 'exponential', 'double_exponential', 'polynomial_degree_1', 'polynomial_degree_2', 'polynomial_degree_3', 'polynomial_degree_4', 'polynomial_degree_5', 'polynomial_degree_6', 'polynomial_degree_7']]

Default value: Unset

noise_factors

Type: UnsetType | Sequence[float]

Default value: Unset

Methods