Noise learning helper

No net ibersetzt

Die Seid isch no net ibersetzt worda. Se gucket die englische Originalversion.

Package versions

The code on this page was developed using the following requirements. We recommend using these versions or newer.

qiskit[all]~=2.3.0
qiskit-ibm-runtime~=0.43.1

The error mitigation techniques PEA and PEC both utilize a noise learning component based on a Pauli-Lindblad noise model, which is typically managed during execution after submitting one or more jobs through qiskit-ibm-runtime without any local access to the fitted noise model. However, as of qiskit-ibm-runtime 0.27.1, a NoiseLearner and associated NoiseLearnerOptions class have been created to obtain the results of these noise learning experiments. These results can then be stored locally as a NoiseLearnerResult and used as input in later experiments. This page provides an overview of its usage and the associated options available.

Overview

The NoiseLearner class performs experiments that characterize noise processes based on a Pauli-Lindblad noise model for one (or more) circuits. It possesses a run() method that executes the learning experiments and takes as input either a list of circuits or a PUB, and returns a NoiseLearnerResult containing the learned noise channels and metadata about the job(s) submitted. Below is a code snippet demonstrating the usage of the helper program.

# Added by doQumentation — required packages for this notebook
!pip install -q qiskit qiskit-ibm-runtime

from qiskit import QuantumCircuit
from qiskit.transpiler import CouplingMap
from qiskit.transpiler import generate_preset_pass_manager

from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2
from qiskit_ibm_runtime.noise_learner import NoiseLearner
from qiskit_ibm_runtime.options import (
    NoiseLearnerOptions,
    ResilienceOptionsV2,
    EstimatorOptions,
)

# Build a circuit with two entangling layers
num_qubits = 27
edges = list(CouplingMap.from_line(num_qubits, bidirectional=False))
even_edges = edges[::2]
odd_edges = edges[1::2]

circuit = QuantumCircuit(num_qubits)
for pair in even_edges:
    circuit.cx(pair[0], pair[1])
for pair in odd_edges:
    circuit.cx(pair[0], pair[1])

# Choose a backend to run on
service = QiskitRuntimeService()
backend = service.least_busy()

# Transpile the circuit for execution
pm = generate_preset_pass_manager(backend=backend, optimization_level=3)
circuit_to_learn = pm.run(circuit)

# Instantiate a NoiseLearner object and execute the noise learning program
learner = NoiseLearner(mode=backend)
job = learner.run([circuit_to_learn])
noise_model = job.result()

The resulting NoiseLearnerResult.data is a list of LayerError objects containing the noise model for each individual entangling layer that belongs to the target circuit(s). Each LayerError stores the layer information, in the form of a circuit and a set of qubit labels, alongside the PauliLindbladError for the noise model that was learned for the given layer.

print(
    f"Noise learner result contains {len(noise_model.data)} entries"
    f" and has the following type:\n {type(noise_model)}\n"
)
print(
    f"Each element of `NoiseLearnerResult` then contains"
    f" an object of type:\n {type(noise_model.data[0])}\n"
)
print(
    f"And each of these `LayerError` objects possess"
    f" data on the generators for the error channel: \n{noise_model.data[0].error.generators}\n"
)
print(f"Along with the error rates: \n{noise_model.data[0].error.rates}\n")

Noise learner result contains 2 entries and has the following type:
 <class 'qiskit_ibm_runtime.utils.noise_learner_result.NoiseLearnerResult'>

Each element of `NoiseLearnerResult` then contains an object of type:
 <class 'qiskit_ibm_runtime.utils.noise_learner_result.LayerError'>

And each of these `LayerError` objects possess data on the generators for the error channel: 
['IIIIIIIIIIIIIIIIIIIIIIIIIIX', 'IIIIIIIIIIIIIIIIIIIIIIIIIIY',
 'IIIIIIIIIIIIIIIIIIIIIIIIIIZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIXI',
 'IIIIIIIIIIIIIIIIIIIIIIIIIXX', 'IIIIIIIIIIIIIIIIIIIIIIIIIXY',
 'IIIIIIIIIIIIIIIIIIIIIIIIIXZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIYI',
 'IIIIIIIIIIIIIIIIIIIIIIIIIYX', 'IIIIIIIIIIIIIIIIIIIIIIIIIYY',
 'IIIIIIIIIIIIIIIIIIIIIIIIIYZ', 'IIIIIIIIIIIIIIIIIIIIIIIIIZI',
 'IIIIIIIIIIIIIIIIIIIIIIIIIZX', 'IIIIIIIIIIIIIIIIIIIIIIIIIZY',
 'IIIIIIIIIIIIIIIIIIIIIIIIIZZ', 'IIIIIIIIIIIIIIIIIIIIIIIIXII',
 'IIIIIIIIIIIIIIIIIIIIIIIIXXI', 'IIIIIIIIIIIIIIIIIIIIIIIIXYI',
 'IIIIIIIIIIIIIIIIIIIIIIIIXZI', 'IIIIIIIIIIIIIIIIIIIIIIIIYII',
 'IIIIIIIIIIIIIIIIIIIIIIIIYXI', 'IIIIIIIIIIIIIIIIIIIIIIIIYYI',
 'IIIIIIIIIIIIIIIIIIIIIIIIYZI', 'IIIIIIIIIIIIIIIIIIIIIIIIZII',
 'IIIIIIIIIIIIIIIIIIIIIIIIZXI', 'IIIIIIIIIIIIIIIIIIIIIIIIZYI',
 'IIIIIIIIIIIIIIIIIIIIIIIIZZI', 'IIIIIIIIIIIIIIIIIIIIIIIXIII',
 'IIIIIIIIIIIIIIIIIIIIIIIXXII', 'IIIIIIIIIIIIIIIIIIIIIIIXYII',
 'IIIIIIIIIIIIIIIIIIIIIIIXZII', 'IIIIIIIIIIIIIIIIIIIIIIIYIII',
 'IIIIIIIIIIIIIIIIIIIIIIIYXII', 'IIIIIIIIIIIIIIIIIIIIIIIYYII',
 'IIIIIIIIIIIIIIIIIIIIIIIYZII', 'IIIIIIIIIIIIIIIIIIIIIIIZIII',
 'IIIIIIIIIIIIIIIIIIIIIIIZXII', 'IIIIIIIIIIIIIIIIIIIIIIIZYII',
 'IIIIIIIIIIIIIIIIIIIIIIIZZII', 'IIIIIIIIIIIIIIIIIIIIIIXIIII',
 'IIIIIIIIIIIIIIIIIIIIIIXXIII', 'IIIIIIIIIIIIIIIIIIIIIIXYIII',
 'IIIIIIIIIIIIIIIIIIIIIIXZIII', 'IIIIIIIIIIIIIIIIIIIIIIYIIII',
 'IIIIIIIIIIIIIIIIIIIIIIYXIII', 'IIIIIIIIIIIIIIIIIIIIIIYYIII',
 'IIIIIIIIIIIIIIIIIIIIIIYZIII', 'IIIIIIIIIIIIIIIIIIIIIIZIIII',
 'IIIIIIIIIIIIIIIIIIIIIIZXIII', 'IIIIIIIIIIIIIIIIIIIIIIZYIII',
 'IIIIIIIIIIIIIIIIIIIIIIZZIII', 'IIIIIIIIIIIIIIIIIIIIIXIIIII',
 'IIIIIIIIIIIIIIIIIIIIIXXIIII', 'IIIIIIIIIIIIIIIIIIIIIXYIIII',
 'IIIIIIIIIIIIIIIIIIIIIXZIIII', 'IIIIIIIIIIIIIIIIIIIIIYIIIII',
 'IIIIIIIIIIIIIIIIIIIIIYXIIII', 'IIIIIIIIIIIIIIIIIIIIIYYIIII',
 'IIIIIIIIIIIIIIIIIIIIIYZIIII', 'IIIIIIIIIIIIIIIIIIIIIZIIIII',
 'IIIIIIIIIIIIIIIIIIIIIZXIIII', 'IIIIIIIIIIIIIIIIIIIIIZYIIII',
 'IIIIIIIIIIIIIIIIIIIIIZZIIII', 'IIIIIIIIIIIIIIIIIIIIXIIIIII',
 'IIIIIIIIIIIIIIIIIIIIYIIIIII', 'IIIIIIIIIIIIIIIIIIIIZIIIIII',
 'IIIIIIIIIIIIIIIIIIIXIIIIIII', 'IIIIIIIIIIIIIIIIIIIXXIIIIII',
 'IIIIIIIIIIIIIIIIIIIXYIIIIII', 'IIIIIIIIIIIIIIIIIIIXZIIIIII',
 'IIIIIIIIIIIIIIIIIIIYIIIIIII', 'IIIIIIIIIIIIIIIIIIIYXIIIIII',
 'IIIIIIIIIIIIIIIIIIIYYIIIIII', 'IIIIIIIIIIIIIIIIIIIYZIIIIII', ...]

Along with the error rates: 
[8.80e-04 6.50e-04 3.10e-04 5.60e-04 0.00e+00 0.00e+00 0.00e+00 3.00e-04
 6.00e-05 1.30e-04 7.00e-05 3.90e-04 0.00e+00 0.00e+00 3.00e-05 3.70e-04
 0.00e+00 5.00e-05 7.50e-04 5.50e-04 5.00e-05 0.00e+00 7.60e-04 5.00e-04
 5.60e-04 5.60e-04 2.50e-04 5.00e-05 7.00e-05 2.00e-04 1.40e-04 8.00e-05
 2.80e-04 0.00e+00 1.70e-04 4.20e-04 3.00e-05 1.00e-05 1.30e-04 4.40e-04
 1.00e-04 2.60e-04 7.10e-04 1.10e-04 2.60e-04 1.00e-04 6.80e-04 1.02e-03
 4.60e-04 5.30e-04 3.00e-04 0.00e+00 0.00e+00 3.40e-04 0.00e+00 0.00e+00
 2.70e-04 0.00e+00 5.00e-05 6.70e-04 0.00e+00 2.20e-04 0.00e+00 4.40e-04
 4.30e-04 8.30e-04 1.42e-03 0.00e+00 0.00e+00 1.44e-03 8.70e-04 0.00e+00
 0.00e+00 1.05e-03 6.80e-04 5.90e-04 5.10e-04 3.10e-04 5.60e-04 0.00e+00
 4.00e-05 0.00e+00 5.50e-04 1.00e-05 2.00e-05 0.00e+00 1.10e-04 0.00e+00
 1.20e-04 0.00e+00 2.20e-04 7.00e-05 4.00e-05 3.80e-04 2.80e-04 4.00e-05
 7.00e-05 3.00e-04 1.20e-04 6.00e-04 5.80e-04 1.80e-04 5.00e-04 1.20e-04
 2.00e-05 2.00e-05 4.80e-04 2.00e-05 0.00e+00 1.40e-04 4.00e-04 3.00e-05
 0.00e+00 0.00e+00 4.40e-04 1.10e-04 5.00e-05 6.00e-04 2.30e-04 5.00e-05
 1.10e-04 5.30e-04 3.60e-04 6.80e-04 6.70e-04 2.80e-04 4.90e-04 1.30e-04
 6.00e-05 7.20e-04 3.00e-05 9.00e-05 1.10e-04 3.30e-04 6.00e-05 1.30e-04
 7.60e-04 1.30e-04 1.50e-04 1.30e-04 0.00e+00 3.10e-04 2.50e-04 5.10e-04
 0.00e+00 6.00e-05 2.50e-04 2.40e-04 8.00e-05 0.00e+00 0.00e+00 2.70e-04
 0.00e+00 8.00e-05 0.00e+00 7.80e-04 7.00e-05 0.00e+00 0.00e+00 2.50e-04
 1.70e-04 2.00e-05 4.50e-04 3.10e-04 2.00e-05 1.70e-04 4.60e-04 1.30e-04
 3.20e-04 3.50e-04 3.80e-04 2.70e-04 2.00e-04 8.00e-05 1.00e-05 4.10e-04
 0.00e+00 0.00e+00 0.00e+00 2.36e-03 0.00e+00 7.00e-05 1.20e-04 9.40e-04
 0.00e+00 1.90e-04 1.38e-03 7.50e-04 1.90e-04 0.00e+00 1.14e-03 7.30e-04
 5.70e-04 4.20e-04 6.20e-04 0.00e+00 2.20e-04 5.00e-05 1.20e-04 0.00e+00
 0.00e+00 1.90e-04 6.00e-05 1.10e-04 2.10e-04 1.50e-04 1.20e-04 2.90e-04
 4.60e-04 2.10e-04 4.00e-05 3.00e-05 1.70e-04 3.10e-04 1.00e-04 1.70e-04
 3.00e-05 3.90e-04 0.00e+00 6.00e-04 5.60e-04 1.40e-04 3.50e-04 1.00e-04
 1.20e-04 9.00e-05 3.20e-04 2.00e-05 1.70e-04 3.00e-05 4.00e-04 1.50e-04
 0.00e+00 1.60e-04 1.90e-04 9.00e-05 6.00e-05 4.50e-04 3.10e-04 6.00e-05
 9.00e-05 3.70e-04 2.80e-04 6.50e-04 5.30e-04 3.30e-04 8.00e-05 8.00e-05
 5.00e-05 2.50e-04 3.50e-04 4.00e-05 0.00e+00 0.00e+00 1.70e-04 1.30e-04
 0.00e+00 0.00e+00 7.00e-05 1.70e-04 1.00e-05 4.20e-04 2.00e-04 1.00e-05
 1.70e-04 4.80e-04 1.40e-03 4.70e-04 4.00e-04 3.90e-04 4.40e-04 2.00e-04
 1.90e-04 7.20e-04 1.80e-04 1.00e-04 0.00e+00 5.70e-04 1.90e-04 2.00e-04
 8.70e-04 1.20e-04 1.70e-04 0.00e+00 0.00e+00 3.80e-04 2.40e-04 4.80e-04
 6.00e-05 0.00e+00 9.00e-05 6.50e-04 2.00e-05 8.00e-05 1.40e-04 5.80e-04
 1.30e-04 0.00e+00 2.00e-05 1.00e-05 1.60e-04 1.00e-05 1.80e-04 4.40e-04
 8.00e-05 1.40e-04 4.40e-04 3.90e-04 1.40e-04 8.00e-05 3.90e-04 4.10e-04
 8.80e-04 7.30e-04 1.90e-04]

The LayerError.error attribute of the noise learning result contains the generators and error rates of the fitted Pauli Lindblad model, which has the form

$\Lambda(\rho) = \exp{\sum_j r_j \left(P_j \rho P_j^\dagger - \rho\right)},$

where the $r_j$ are the LayerError.rates and $P_j$ are the Pauli operators specified in LayerError.generators.

Noise learning options

You can choose among several options to input when you instantiate a NoiseLearner object. These options are encapsulated by the qiskit_ibm_runtime.options.NoiseLearnerOptions class and include the ability to specify the maximum layers to learn, number of randomizations, and the twirling strategy, among others. Refer to the API documentation about NoiseLearnerOptions for more detailed information.

Below is a simple example showing how to use the NoiseLearnerOptions in a NoiseLearner experiment:

# Build a GHZ circuit
circuit = QuantumCircuit(10)
circuit.h(0)
circuit.cx(range(0, 9), range(1, 10))
# Choose a backend to run on
service = QiskitRuntimeService()
backend = service.least_busy()

# Transpile the circuit for execution
pm = generate_preset_pass_manager(backend=backend, optimization_level=3)
circuit_to_run = pm.run(circuit_to_learn)

# Instantiate a noise learner options object
learner_options = NoiseLearnerOptions(
    max_layers_to_learn=3, num_randomizations=32, twirling_strategy="all"
)

# Instantiate a NoiseLearner object and execute the noise learning program
learner = NoiseLearner(mode=backend, options=learner_options)
job = learner.run([circuit_to_run])
noise_model = job.result()

Input noise model to a primitive

The noise model learned on the circuit can also be used as an input to the EstimatorV2 primitive implemented in Qiskit IBM Runtime. This can be passed into the primitive a few different ways. The next three examples show how you can pass the noise model to the estimator.options attribute directly, via a ResilienceOptionsV2 object before instantiating an Estimator primitive, and by passing in an appropriately formatted dictionary.

# pass the noise model to the `estimator.options` attribute directly
estimator = EstimatorV2(mode=backend)
estimator.options.resilience.layer_noise_model = noise_model

# Specify options via a ResilienceOptionsV2 object
resilience_options = ResilienceOptionsV2(layer_noise_model=noise_model)
estimator_options = EstimatorOptions(resilience=resilience_options)
estimator = EstimatorV2(mode=backend, options=estimator_options)

# Specify options via a dictionary
options_dict = {
    "resilience_level": 2,
    "resilience": {"layer_noise_model": noise_model},
}

estimator = EstimatorV2(mode=backend, options=options_dict)

Once the noise model is passed into the EstimatorV2 object, it can be used to run workloads and perform error mitigation as normal.

Next steps

Recommendations

Read more about configuring error mitigation.
Review the EstimatorOptions API reference and ResilienceOptionsV2 API reference.
Learn more about Error mitigation and suppression techniques that are available through Qiskit Runtime.
Review how to Specify options for the Qiskit Runtime primitives.
Read Migrate to V2 primitives.

Overview​

Noise learning options​

Input noise model to a primitive​

Next steps​

Overview

Noise learning options

Input noise model to a primitive

Next steps