NoiseLearner

class NoiseLearner(mode=None, options=None)

Bases: object

Class for executing noise learning experiments.

The noise learner class allows characterizing the noise processes affecting the gates in one or more circuits of interest, based on the Pauli-Lindblad noise model described in [1].

The run() method allows runnig a noise learner job for a list of circuits. After the job is submitted, the gates are collected into independent layers, and subsequently the resulting layers are are characterized individually.

The way in which the gates are collected into layers depends on the twirling strategy specified in the given options (see NoiseLearnerOptions for more details). Note that all strategies obey barriers. For example, if you have three ISA entangling layers of interest, consider putting them into one circuit separated by barriers acting on the qubits you wish to twirl, and select strategy="active-circuit".

The following snippet shows an example where the noise learner is used to characterized the layers of two GHZ circuits.

from qiskit.circuit import QuantumCircuit
from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
from qiskit_ibm_runtime import QiskitRuntimeService
from qiskit_ibm_runtime.noise_learner import NoiseLearner
from qiskit_ibm_runtime.options import NoiseLearnerOptions
 
service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False)
 
# a circuit returning a two-qubit GHZ state
ghz = QuantumCircuit(2)
ghz.h(0)
ghz.cx(0, 1)
 
# another circuit returning a two-qubit GHZ state
another_ghz = QuantumCircuit(3)
another_ghz.h(0)
another_ghz.cx(0, 1)
another_ghz.cx(1, 2)
another_ghz.cx(0, 1)
 
pm = generate_preset_pass_manager(backend=backend, optimization_level=1)
circuits = pm.run([ghz, another_ghz])
 
# set the options
options = NoiseLearnerOptions()
options.layer_pair_depths = [0, 1, 10]
 
# run the noise learner job
learner = NoiseLearner(backend, options)
job = learner.run(circuits)

Parameters

• mode (Optional[Union[BackendV2, Session, Batch]]) –

  The execution mode used to make the primitive query. It can be:

  • A Backend if you are using job mode.
  • A Session if you are using session execution mode.
  • A Batch if you are using batch execution mode.

  Refer to the Qiskit Runtime documentation for more information about the execution modes.

• options (Optional[Union[Dict, NoiseLearnerOptions, EstimatorOptions]]) – NoiseLearnerOptions. Alternatively, EstimatorOptions can be provided for convenience, in which case the estimator options get reformatted into noise learner options and all the irrelevant fields are ignored.

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

options

Methods

backend

run