NoiseLearnerOptions

class NoiseLearnerOptions(_VERSION=2, max_execution_time=Unset, environment=<factory>, simulator=<factory>, max_layers_to_learn=Unset, shots_per_randomization=Unset, num_randomizations=Unset, layer_pair_depths=Unset, twirling_strategy=Unset, experimental=Unset)[source]

Bases: OptionsV2

Options for NoiseLearner.

Note

The total number of unique circuits implemented to learn the noise of a single layer depends solely on layer_pair_depths and num_randomizations. For example, if layer_pair_depths contains six depths and num_randomizations is set to 32, the noise learning stage executes a total of 6 * 9 unique circuits per layer, each one with 32 randomizations (at shots_per_randomization each).

The number 9 above is the number of unique circuits that need to be implemented to learn the noise for all the two-qubit subsystem in the given layer by performing local measurements. Indeed, learning the noise for a single one of these subsystems requires measuring all the 16 two-qubit Paulis on that subsystem. Taking advantage of commutation relations to measure more than one of these Paulis (for example, XI, IX, and XX) with a single circuit, it is possible to measure all these 16 Paulis by implementing only 9 circuits. Parallelizing these measurement tasks in the optimal way allows then measuring the 16 Paulis for all of the layer’s two-qubit subsystems with only 9 circuits. More details in Ref. [1].

References

  1. E. van den Berg, Z. Minev, A. Kandala, K. Temme, Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors, Nature Physics volume 19, pages 1116–1121 (2023). arXiv:2201.09866 [quant-ph]

Attributes

Parameters:

  • _VERSION (int)

  • max_execution_time (UnsetType | int)

  • environment (EnvironmentOptions | Dict)

  • simulator (SimulatorOptions | Dict)

  • max_layers_to_learn (UnsetType | int | None)

  • shots_per_randomization (UnsetType | int)

  • num_randomizations (UnsetType | int)

  • layer_pair_depths (UnsetType | List[int])

  • twirling_strategy (UnsetType | Literal['active', 'active-accum', 'active-circuit', 'all'])

  • experimental (UnsetType | dict)

environment: EnvironmentOptions | Dict = FieldInfo(annotation=NoneType, required=False, default_factory=EnvironmentOptions)
experimental: UnsetType | dict = Unset

Experimental options.

These options are subject to change without notification, and stability is not guaranteed.

layer_pair_depths: UnsetType | List[int] = Unset

The circuit depths (measured in number of pairs) to use in learning experiments.

Pairs are used as the unit because we exploit the order-2 nature of our entangling gates in the noise learning implementation. For example, a value of 3 corresponds to 6 repetitions of the layer of interest. Default: (0, 1, 2, 4, 16, 32).

max_execution_time: UnsetType | int = Unset
max_layers_to_learn: UnsetType | int | None = Unset

The max number of unique layers to learn.

A None value indicates that there is no limit. If there are more unique layers present, then some layers will not be learned or mitigated. The learned layers are prioritized based on the number of times they occur, and for equally occurring layers are further sorted by the number of two-qubit gates in the layer. Default: 4.

num_randomizations: UnsetType | int = Unset

The number of random circuits to use per learning circuit configuration.

A configuration is a measurement basis and depth setting. For example, if your experiment has six depths, then setting this value to 32 will result in a total of 32 * 9 * 6 circuits that need to be executed (where 9 is the number of circuits that need to be implemented to measure all the required observables, see the note in the docstring for NoiseLearnerOptions for mode details), at shots_per_randomization each.

shots_per_randomization: UnsetType | int = Unset

The total number of shots to use per random learning circuit.

A learning circuit is a random circuit at a specific learning depth with a specific measurement basis that is executed on hardware. Default: 128.

simulator: SimulatorOptions | Dict = FieldInfo(annotation=NoneType, required=False, default_factory=SimulatorOptions)
twirling_strategy: UnsetType | Literal['active', 'active-accum', 'active-circuit', 'all'] = Unset

The twirling strategy in the identified layers of two-qubit twirled gates.

The allowed values are:

  • "active": in each individual twirled layer, only the instruction qubits are twirled.

  • "active-circuit": in each individual twirled layer, the union of all instruction

    qubits in the circuit are twirled.

  • "active-accum": in each individual twirled layer, the union of instructions qubits

    in the circuit up to the current twirled layer are twirled.

  • "all": in each individual twirled layer, all qubits in the input circuit are twirled.

Note

Barriers and delay instructions are ignored when determining whether a qubit is active.

Default: “active-accum”.

Methods

update(**kwargs)[source]

Update the options.

Parameters:

kwargs (Any)

Return type:

None