iqm.benchmarks.quantum_volume.clops.CLOPSConfiguration

class iqm.benchmarks.quantum_volume.clops.CLOPSConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.quantum_volume.clops.CLOPSBenchmark'>, 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: ~typing.Sequence[int], num_circuits: int = 100, num_updates: int = 10, num_shots: int = 100, clops_h_bool: bool = False, qiskit_optim_level: int = 3, optimize_sqg: bool = True)

Bases: BenchmarkConfigurationBase

CLOPS 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 (Sequence[int]) –

• num_circuits (int) –

• num_updates (int) –

• num_shots (int) –

• clops_h_bool (bool) –

• qiskit_optim_level (int) –

• optimize_sqg (bool) –

benchmark

CLOPS Benchmark.

Type:

Type[Benchmark]

qubits

The Sequence (List or Tuple) of physical qubit labels in which to run the benchmark. * The physical qubit layout should correspond to the one used to establish QV.

Type:

Sequence[int]

num_circuits

The number of parametrized circuit layouts. * By definition of arXiv:2110.14108 [quant-ph] set to 100.

Type:

int

num_updates

The number of parameter assignment updates per circuit layout to perform. * By definition of arXiv:2110.14108 [quant-ph] set to 10.

Type:

int

num_shots

The number of measurement shots per circuit to perform. * By definition of arXiv:2110.14108 [quant-ph] set to 100.

Type:

int

clops_h_bool

Whether a CLOPS value with layer definition of CLOPS_H is estimated. * Default is False * This will not estimate a rigorous CLOPS_H value (as loosely defined in www.ibm.com/quantum/blog/quantum-metric-layer-fidelity)

Type:

bool

qiskit_optim_level

The Qiskit transpilation optimization level. * The optimization level should correspond to the one used to establish QV. * Default is 3.

Type:

int

optimize_sqg

Whether Single Qubit Gate Optimization is performed upon transpilation. * The optimize_sqg value should correspond to the one used to establish QV. * Default is True

Type:

bool

routing_method

The Qiskit transpilation routing method to use. * The routing_method value should correspond to the one used to establish QV. * Default is “sabre”.

Type:

Literal[“basic”, “lookahead”, “stochastic”, “sabre”, “none”]

physical_layout

Whether the coupling map is restricted to qubits in the input layout or not. - “fixed”: Restricts the coupling map to only the specified qubits. - “batching”: Considers the full coupling map of the backend and circuit execution is batched per final layout. * Default is “fixed”.

Type:

Literal[“fixed”, “batching”]

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
qubits
num_circuits
num_updates
num_shots
clops_h_bool
qiskit_optim_level
optimize_sqg
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].