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

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.
name()	Returns the name of the 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

classmethod name() → str

Returns the name of the benchmark.

Return type:

str

add_all_meta_to_dataset(dataset: Dataset) → None

Adds all configuration metadata and circuits to the dataset variable.

Parameters:

dataset (Dataset) – The xarray dataset

Return type:

None

validate_custom_qubits_array() → None

Validates the custom qubits array input .

Return type:

None

validate_random_chain_inputs() → None

Validates inputs for chain sampling.

Raises:

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

Return type:

None

execute(backend: IQMBackendBase) → Dataset

Execute the EPLG Benchmark.

Parameters:

backend (IQMBackendBase)

Return type:

Dataset