iqm.benchmarks.randomized_benchmarking.eplg.eplg.EPLGConfiguration

class iqm.benchmarks.randomized_benchmarking.eplg.eplg.EPLGConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.randomized_benchmarking.eplg.eplg.EPLGBenchmark'>, shots: int = 256, max_gates_per_batch: int | None = None, max_circuits_per_batch: int | None = None, calset_id: str | None = None, routing_method: ~typing.Literal['basic', 'lookahead', 'stochastic', 'sabre', 'none'] = 'sabre', physical_layout: ~typing.Literal['fixed', 'batching'] = 'fixed', use_dd: bool | None = False, dd_strategy: ~iqm.iqm_client.models.DDStrategy | None = None, drb_depths: ~typing.Sequence[int], drb_circuit_samples: int, custom_qubits_array: ~typing.Sequence[~typing.Tuple[int, int]] | None = None, chain_length: int | None = None, chain_path_samples: int | None = None, num_disjoint_layers: int | None = None, max_hamiltonian_path_tries: int | None = None, calibration_url: str | None = None)

Bases: BenchmarkConfigurationBase

EPLG Configuration

Parameters:

• benchmark (Type[Benchmark]) –

• shots (int) –

• max_gates_per_batch (int | None) –

• max_circuits_per_batch (int | None) –

• calset_id (str | None) –

• routing_method (Literal['basic', 'lookahead', 'stochastic', 'sabre', 'none']) –

• physical_layout (Literal['fixed', 'batching']) –

• use_dd (bool | None) –

• dd_strategy (DDStrategy | None) –

• drb_depths (Sequence[int]) –

• drb_circuit_samples (int) –

• custom_qubits_array (Sequence[Tuple[int, int]] | None) –

• chain_length (int | None) –

• chain_path_samples (int | None) –

• num_disjoint_layers (int | None) –

• max_hamiltonian_path_tries (int | None) –

• calibration_url (str | None) –

drb_depths

The layer depths to consider for the parallel DRB.

Type:

Sequence[int]

drb_circuit_samples

The number of circuit samples to consider for the parallel DRB.

Type:

int

custom_qubits_array

The custom qubits array to consider; this corresponds to a Sequence of Tuple pairs of qubits. * If not specified, will proceed to generate linear chains at random, selecting the one with the highest total 2Q gate fidelity. * Default is None.

Type:

Optional[Sequence[Tuple[int, int]]]

chain_length

The length of a linear chain of 2Q gates to consider, corresponding to the number of qubits, if custom_qubits_array not specified. * Default is None: assigns the number of qubits in the backend minus one.

Type:

Optional[int]

chain_path_samples

The number of chain path samples to consider, if custom_qubits_array not specified. * Default is None: assigns 20 path samples (arbitrary).

Type:

int

num_disjoint_layers

The number of disjoint layers to consider. * Default is None: assigns 2 disjoint layers (arbitrary).

Type:

Optional[int]

max_hamiltonian_path_tries

The maximum number of tries to find a Hamiltonian path. * Default is None: assigns 10 tries (arbitrary).

Type:

Optional[int]

calibration_url

The URL of the IQM station to retrieve calibration data from. * It must be specified if custom_qubits_array is not specified. * Default is None - raises an error if custom_qubits_array is not specified.

Type:

Optional[str]

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
drb_depths
drb_circuit_samples
custom_qubits_array
chain_length
chain_path_samples
num_disjoint_layers
max_hamiltonian_path_tries
calibration_url
shots
max_gates_per_batch
max_circuits_per_batch
calset_id
routing_method
physical_layout
use_dd
dd_strategy

Methods

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].