iqm.benchmarks.entanglement.graph_states.GraphStateConfiguration

class iqm.benchmarks.entanglement.graph_states.GraphStateConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.entanglement.graph_states.GraphStateBenchmark'>, 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], tomography: ~typing.Literal['state_tomography', 'shadow_tomography'] = 'state_tomography', num_bootstraps: int = 50, n_random_unitaries: int = 100, n_median_of_means: int = 1)

Bases: BenchmarkConfigurationBase

Graph States Benchmark 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]) –

• tomography (Literal['state_tomography', 'shadow_tomography']) –

• num_bootstraps (int) –

• n_random_unitaries (int) –

• n_median_of_means (int) –

benchmark

GraphStateBenchmark

Type:

Type[Benchmark]

qubits

The physical qubit layout in which to benchmark graph state generation.

Type:

Sequence[int]

tomography

Whether to use state or shadow tomography. * Default is “state_tomography”.

Type:

Literal[“state_tomography”, “shadow_tomography”]

num_bootstraps

The amount of bootstrap samples to use with state tomography. * Default is 50.

Type:

int

n_random_unitaries

The number of Haar random single-qubit unitaries to use for (local) shadow tomography. * Default is 100.

Type:

int

n_median_of_means

The number of mean samples over n_random_unitaries to generate a median of means estimator for shadow tomography. * NB: The total amount of execution calls will be a multiplicative factor of n_random_unitaries x n_median_of_means. * Default is 1 (no median of means).

Type:

int

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
qubits
tomography
num_bootstraps
n_random_unitaries
n_median_of_means
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].