ZneOptions

class ZneOptions(amplifier=Unset, noise_factors=Unset, extrapolator=Unset, extrapolated_noise_factors=Unset)

Bases: object

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

Parameters

• amplifier (UnsetType | Literal['gate_folding', 'gate_folding_front', 'gate_folding_back', 'pea']) –

  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) 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 (UnsetType | Sequence[float]) – Noise factors to use for noise amplification. Default: (1, 1.5, 2) for PEA, and (1, 3, 5) otherwise.

• extrapolated_noise_factors (UnsetType | Sequence[float]) – 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 extrapolators are evaluated to be returned in the data fields called evs_extrapolated and stds_extrapolated.

• extrapolator (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', 'fallback'] | ~typing.Sequence[~typing.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', 'fallback']]) –

  Extrapolator(s) to try (in order) for extrapolating to zero noise. The available options are:

  • "exponential", which fits the data using an exponential decaying function defined as $f(x; A, \tau) = A e^{-x/\tau}$, where $A = f(0; A, \tau)$ is the value at zero noise ($x=0$) and $\tau>0$ is a positive rate.
  • "double_exponential", which uses a sum of two exponential as in Ref. 1.
  • "polynomial_degree_(1 <= k <= 7)", which uses a polynomial function defined as $f(x; c_0, c_1, \ldots, c_k) = \sum_{i=0, k} c_i x^i$.
  • "linear", which is equivalent to "polynomial_degree_1".
  • "fallback", which simply returns the raw data corresponding to the lowest noise factor (typically 1) without performing any sort of extrapolation.

  If more than one extrapolator is specified, the evs and stds reported in the result’s data refer to the first one, while the extrapolated values (evs_extrapolated and stds_extrapolated) are sorted according to the order of the extrapolators provided.

  Default: ("exponential", "linear").

References

1. Z. Cai, Multi-exponential error extrapolation and combining error mitigation techniques for NISQ applications, npj Quantum Inf 7, 80 (2021)

Attributes

amplifier

extrapolated_noise_factors

extrapolator

noise_factors

Methods