iqm.benchmarks.coherence.coherence.CoherenceConfiguration

class iqm.benchmarks.coherence.coherence.CoherenceConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.coherence.coherence.CoherenceBenchmark'>, shots: int = 1000, 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, delays: list[float], optimize_sqg: bool = True, qiskit_optim_level: int = 3, coherence_exp: str = 't1', qubits_to_plot: list[int])

Bases: BenchmarkConfigurationBase

Coherence 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) –

• delays (list[float]) –

• optimize_sqg (bool) –

• qiskit_optim_level (int) –

• coherence_exp (str) –

• qubits_to_plot (list[int]) –

benchmark

The benchmark class used for coherence analysis, defaulting to CoherenceBenchmark.

Type:

Type[Benchmark]

delays

List of delay times used in the coherence experiments.

Type:

list[float]

qiskit_optim_level

Qiskit transpilation optimization level, default is 3.

Type:

int

optimize_sqg

Indicates whether Single Qubit Gate Optimization is applied during transpilation, default is True.

Type:

bool

coherence_exp

Specifies the type of coherence experiment, either “t1” or “echo”, default is “t1”.

Type:

str

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
delays
optimize_sqg
qiskit_optim_level
coherence_exp
shots
qubits_to_plot
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].