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IBM Quantum Platform

NoiseLearnerV3

class NoiseLearnerV3(mode=None, options=None)

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Bases: object

Class for executing noise learning experiments.

The noise learner allows characterizing the noise processes affecting target instructions, based on the Pauli-Lindblad noise model described in [1]. The instructions provided to the run() method must contain a twirled-annotated BoxOp containing ISA operations. The result of a noise learner job contains a list of NoiseLearnerV3Result objects, one for each given instruction.

Parameters

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

mode

Return the execution mode used by this primitive.

Returns

Mode used by this primitive, or None if an execution mode is not used.

options

Type: NoiseLearnerV3Options

The options in this noise learner.


Methods

backend

backend()

GitHub

Return the backend the primitive query will be run on.

Return type

BackendV2

run

run(instructions, dry_run=False)

GitHub

Submit a request to the noise learner program.

Parameters

  • instructions (Iterable[CircuitInstruction]) – The instructions to learn the noise of.
  • dry_run (bool) – If True, performs a dry run without executing the job on a QPU. This mode can be used to validate the job, estimate usage consumption, and retrieve circuit timing metadata. Returned results preserve the expected schema but contain randomized mock data rather than actual or simulated measurement results. Unlike the fake backends, the processing of this dry run happens on the server-side, so the job may not finish immediately and access to this feature may be restricted.

Returns

The submitted job.

Return type

RuntimeJobV2 | LocalRuntimeJob

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