iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRBConfiguration

class iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRBConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRandomizedBenchmarking'>, 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, qubits_array: ~typing.Sequence[~typing.Sequence[int]], sequence_lengths: ~typing.Sequence[int], num_circuit_samples: int, parallel_execution: bool = False)

Bases: BenchmarkConfigurationBase

Clifford RB 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) –

• qubits_array (Sequence[Sequence[int]]) –

• sequence_lengths (Sequence[int]) –

• num_circuit_samples (int) –

• parallel_execution (bool) –

benchmark

CliffordRandomizedBenchmarking

Type:

Type[Benchmark]

qubits_array

The array of qubits on which to execute the benchmark.

Type:

Sequence[Sequence[int]]

sequence_lengths

The length of Cliffords sequences with which to execute benchmark.

Type:

Sequence[int]

num_circuit_samples

The number of Cliffords circuits per sequence length.

Type:

int

shots

The number of measurement shots per circuit.

Type:

int

parallel_execution

Whether the benchmark is executed on all qubits in parallel or not. * Default is False.

Type:

bool

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
qubits_array
sequence_lengths
num_circuit_samples
parallel_execution
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].