iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb.MirrorRandomizedBenchmarking#

class iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb.MirrorRandomizedBenchmarking(backend_arg: IQMBackendBase | str, configuration: MirrorRBConfiguration)#

Bases: Benchmark

Mirror RB estimates the fidelity of ensembles of n-qubit layers

Attributes

name

Methods

add_all_meta_to_dataset(dataset)

Adds all configuration metadata and circuits to the dataset variable

analysis_function(run)

Analysis function for a MRB experiment

execute(backend)

Executes the benchmark

submit_single_mrb_job(backend_arg, qubits, ...)

Submit fixed-depth MRB jobs for execution in the specified IQMBackend

Parameters:
static analysis_function(run: BenchmarkRunResult) BenchmarkAnalysisResult#

Analysis function for a MRB experiment

Parameters:

run (RunResult) – A MRB experiment run for which analysis result is created

Returns:

AnalysisResult corresponding to MRB

Return type:

BenchmarkAnalysisResult

add_all_meta_to_dataset(dataset: Dataset)#

Adds all configuration metadata and circuits to the dataset variable

Parameters:

dataset (xr.Dataset) – The xarray dataset

submit_single_mrb_job(backend_arg: IQMBackendBase, qubits: Sequence[int], depth: int, sorted_transpiled_circuit_dicts: Dict[Tuple[int, ...], List[IQMCircuit]]) Dict[str, Any]#

Submit fixed-depth MRB jobs for execution in the specified IQMBackend

Parameters:
  • backend_arg (IQMBackendBase) – the IQM backend to submit the job

  • qubits (Sequence[int]) – the qubits to identify the submitted job

  • depth (int) – the depth (number of canonical layers) of the circuits to identify the submitted job

  • sorted_transpiled_circuit_dicts (Dict[str, List[QuantumCircuit]]) – A dictionary containing all MRB circuits

Returns:

Dict with qubit layout, submitted job objects, type (vanilla/DD) and submission time

Return type:

Dict[str, Any]

execute(backend: IQMBackendBase) Dataset#

Executes the benchmark

Parameters:

backend (IQMBackendBase) –

Return type:

Dataset