Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

LayerFidelity

class LayerFidelity(physical_qubits, two_qubit_layers, lengths, backend=None, num_samples=6, seed=None, two_qubit_gate=None, one_qubit_basis_gates=None)[source]

A holistic benchmarking experiment to characterize the full quality of the devices at scale.

Overview

Layer Fidelity (LF) is a method to estimate the fidelity of a connecting set of two-qubit gates over \(N\) qubits by measuring gate errors using simultaneous direct randomized benchmarking (RB) in disjoint layers. LF can easily be expressed as a layer size independent quantity, error per layered gate (EPLG): \(EPLG = 1 - LF^{1/N_{2Q}}\) where \(N_{2Q}\) is number of 2-qubit gates in the layers.

Each of the 2-qubit (or 1-qubit) direct RBs yields the decaying probabilities to get back to the ground state for an increasing sequence length (i.e. number of layers), fits the exponential curve to estimate the decay rate, and calculates the process fidelity of the subsystem from the rate. LF is calculated as the product of the 2-qubit (or 1-qubit) process fidelities. See Ref. [1] for details.

References

[1] David C. McKay, Ian Hincks, Emily J. Pritchett, Malcolm Carroll, Luke C. G. Govia, Seth T. Merkel, Benchmarking Quantum Processor Performance at Scale (open)

Analysis class reference

LayerFidelityAnalysis

Experiment options

These options can be set by the set_experiment_options() method.

Options

  • Defined in the class LayerFidelity:

    • two_qubit_layers (List[List[Tuple[int, int]]])

      Default value: None
      List of two-qubit gate layers to run on. Each two-qubit gate layer must be given as a list of directed qubit pairs.

    • lengths (List[int])

      Default value: None
      A list of layer lengths.

    • num_samples (int)

      Default value: None
      Number of samples to generate for each layer length.

    • seed (None or int or SeedSequence or BitGenerator or Generator)

      Default value: None
      A seed used to initialize numpy.random.default_rng when generating circuits. The default_rng will be initialized with this seed value every time circuits() is called.

    • two_qubit_gate (str)

      Default value: None
      Two-qubit gate name (e.g. “cx”, “cz”, “ecr”) of which the two qubit layers consist.

    • one_qubit_basis_gates (Tuple[str])

      Default value: None
      One-qubit gates to use for implementing 1q Cliffords.

    • clifford_synthesis_method (str)

      Default value: "rb_default"
      The name of the Clifford synthesis plugin to use for building circuits of RB sequences.

  • Defined in the class BaseExperiment:

    • max_circuits (Optional[int])

      Default value: None
      The maximum number of circuits per job when running an experiment on a backend.

Example

import numpy as np
from qiskit_experiments.library import StandardRB
from qiskit_experiments.library.randomized_benchmarking import LayerFidelity

lengths = np.arange(1, 800, 200)
two_qubit_layers=[[(0, 1), (3, 5)], [(1, 3), (5, 6)]]

num_samples = 6
seed = 106

exp = LayerFidelity(
        physical_qubits=[0, 1, 3, 5, 6],
        two_qubit_layers=two_qubit_layers,
        lengths=lengths,
        backend=backend,
        num_samples=num_samples,
        seed=seed,
        two_qubit_gate=None,
        one_qubit_basis_gates=None,
)

exp_data = exp.run().block_for_results()
results = exp_data.analysis_results()

display(exp_data.figure(0)) # one of 6 figures
display(exp_data.analysis_results("EPLG", dataframe=True))

names={result.name for result in results}
print(f"Available results: {names}")
../_images/qiskit_experiments.library.randomized_benchmarking.LayerFidelity_1_0.png
name experiment components value quality backend run_time qubits reason a alpha b chisq
8d0f0d8a EPLG LayerFidelity [Q0, Q1, Q3, Q5, Q6] 0.0117+/-0.0005 bad aer_simulator_from(fake_perth) None None None None None None None 
Available results: {'@Parameters__ProcessFidelityAnalysis', 'SingleLF', 'ProcessFidelity', 'LF', 'alpha', 'EPLG'}

Initialization

Initialize a layer fidelity experiment.

Parameters:

  • physical_qubits (Sequence[int]) – List of physical qubits for the experiment.

  • two_qubit_layers (Sequence[Sequence[Tuple[int, int]]]) – List of two-qubit gate layers to run on. Each two-qubit gate layer must be given as a list of directed qubit pairs.

  • lengths (Iterable[int]) – A list of layer lengths (the number of depth points).

  • backend (Backend | None) – The backend to run the experiment on. Note that either backend or two_qubit_gate and one_qubit_basis_gates must be set at instantiation.

  • num_samples (int) – Number of samples (i.e. circuits) to generate for each layer length.

  • seed (int | SeedSequence | BitGenerator | Generator | None) – Optional, seed used to initialize numpy.random.default_rng. when generating circuits. The default_rng will be initialized with this seed value every time :meth:~.LayerFidelity.circuits` is called.

  • two_qubit_gate (str | None) – Optional, 2q-gate name (e.g. “cx”, “cz”, “ecr”) of which the two qubit layers consist. If not specified (but backend is supplied), one of 2q-gates supported in the backend is automatically set.

  • one_qubit_basis_gates (Sequence[str] | None) – Optional, 1q-gates to use for implementing 1q-Clifford operations. If not specified (but backend is supplied), all 1q-gates supported in the backend are automatically set.

Raises:

QiskitError – If any invalid argument is supplied.

Attributes

analysis: BaseAnalysis

Return the analysis instance for the experiment

backend

Return the backend for the experiment

experiment_options

Return the options for the experiment.

experiment_type

Return experiment type.

num_qubits

Return the number of qubits for the experiment.

physical_qubits

Return the device qubits for the experiment.

run_options

Return options values for the experiment run() method.

transpile_options

Return the transpiler options for the run() method.

Methods

circuits()[source]

Return a list of physical circuits to measure layer fidelity.

Returns:

A list of QuantumCircuits.

Return type:

List[QuantumCircuit]

circuits_generator()[source]

Return a generator of physical circuits to measure layer fidelity.

Returns:

A generator of QuantumCircuits.

Return type:

Iterable[QuantumCircuit]

config()

Return the config dataclass for this experiment

Return type:

ExperimentConfig

copy()

Return a copy of the experiment

Return type:

BaseExperiment

enable_restless(rep_delay=None, override_processor_by_restless=True, suppress_t1_error=False)

Enables a restless experiment by setting the restless run options and the restless data processor.

Deprecated since version 0.8: The method qiskit_experiments.framework.restless_mixin.RestlessMixin.enable_restless() is deprecated as of qiskit-experiments 0.8. It will be removed no earlier than 3 months after the release date. Support for restless experiments has been deprecated.

Parameters:

  • rep_delay (float | None) – The repetition delay. This is the delay between a measurement and the subsequent quantum circuit. Since the backends have dynamic repetition rates, the repetition delay can be set to a small value which is required for restless experiments. Typical values are 1 us or less.

  • override_processor_by_restless (bool) – If False, a data processor that is specified in the analysis options of the experiment is not overridden by the restless data processor. The default is True.

  • suppress_t1_error (bool) – If True, the default is False, then no error will be raised when rep_delay is larger than the T1 times of the qubits. Instead, a warning will be logged as restless measurements may have a large amount of noise.

Raises:

  • DataProcessorError – If the attribute rep_delay_range is not defined for the backend.

  • DataProcessorError – If a data processor has already been set but override_processor_by_restless is True.

  • DataProcessorError – If the experiment analysis does not have the data_processor option.

  • DataProcessorError – If the rep_delay is equal to or greater than the T1 time of one of the physical qubits in the experiment and the flag ignore_t1_check is False.

classmethod from_config(config)[source]

Initialize an experiment from experiment config

Return type:

LayerFidelity

job_info(backend=None)

Get information about job distribution for the experiment on a specific backend.

Parameters:

backend (Backend) – Optional, the backend for which to get job distribution information. If not specified, the experiment must already have a set backend.

Returns:

A dictionary containing information about job distribution.

  • ”Total number of circuits in the experiment”: Total number of circuits in the experiment.

  • ”Maximum number of circuits per job”: Maximum number of circuits in one job based on backend and experiment settings.

  • ”Total number of jobs”: Number of jobs needed to run this experiment on the currently set backend.

Return type:

dict

Raises:

QiskitError – if backend is not specified.

run(backend=None, sampler=None, analysis='default', timeout=None, backend_run=None, **run_options)

Run an experiment and perform analysis.

Parameters:

  • backend (Backend | None) – Optional, the backend to run on. Will override existing backend settings.

  • sampler (BaseSamplerV2 | None) – Optional, the sampler to run the experiment on. If None then a sampler will be invoked from previously set backend

  • analysis (BaseAnalysis | None) – Optional, a custom analysis instance to use for performing analysis. If None analysis will not be run. If "default" the experiments analysis() instance will be used if it contains one.

  • timeout (float | None) – Time to wait for experiment jobs to finish running before cancelling.

  • backend_run (bool | None) – Use backend run (temp option for testing)

  • run_options – backend runtime options used for circuit execution.

Returns:

The experiment data object.

Raises:

QiskitError – If experiment is run with an incompatible existing ExperimentData container.

Return type:

ExperimentData

set_experiment_options(**fields)[source]

Set the experiment options.

Parameters:

fields – The fields to update the options

Raises:

AttributeError – If the field passed in is not a supported options

set_run_options(**fields)

Set options values for the experiment run() method.

Parameters:

fields – The fields to update the options

See also

The Setting options for your experiment guide for code example.

set_transpile_options(**fields)[source]

Transpile options is not supported for LayerFidelity experiments.

Raises:

QiskitError – If set_transpile_options is called.