iqm.benchmarks.randomized_benchmarking.eplg.eplg.EPLGBenchmark

class iqm.benchmarks.randomized_benchmarking.eplg.eplg.EPLGBenchmark(backend_arg: IQMBackendBase | str, configuration: EPLGConfiguration)

Bases: Benchmark

EPLG estimates the layer fidelity of native 2Q gate layers

Attributes

name

Methods

add_all_meta_to_dataset(dataset)	Adds all configuration metadata and circuits to the dataset variable
analysis_function(run)	EPLG analysis function
execute(backend)	Execute the EPLG Benchmark
validate_custom_qubits_array()	Validates the custom qubits array input .
validate_random_chain_inputs()	Validates inputs for chain sampling.

Parameters:

• backend_arg (IQMBackendBase | str) –

• configuration (EPLGConfiguration) –

static analysis_function(run: BenchmarkRunResult) → BenchmarkAnalysisResult

EPLG analysis function

Parameters:

run (BenchmarkRunResult) – The result of the benchmark run.

Returns:

AnalysisResult corresponding to DRB.

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

validate_custom_qubits_array()

Validates the custom qubits array input .

validate_random_chain_inputs()

Validates inputs for chain sampling.

Raises:

ValueError – If the chain inputs are beyond general or EPLG criteria.

execute(backend: IQMBackendBase) → Dataset

Execute the EPLG Benchmark

Parameters:

backend (IQMBackendBase) –

Return type:

Dataset