iqm.benchmarks.entanglement.graph_states.GraphStateConfiguration

iqm.benchmarks.entanglement.graph_states.GraphStateConfiguration#

class iqm.benchmarks.entanglement.graph_states.GraphStateConfiguration(*, benchmark: 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, routing_method: ~iqm.benchmarks.utils.RoutingMethod = RoutingMethod.SABRE, physical_layout: ~iqm.benchmarks.utils.PhysicalLayout = PhysicalLayout.FIXED, use_dd: bool | None = False, dd_strategy: ~iqm.station_control.interface.models.circuit.DDStrategy | None = None, active_reset_cycles: int | None = None, qubits: ~collections.abc.Sequence[int], tomography: ~iqm.benchmarks.entanglement.graph_states.TomographyType = TomographyType.STATE, num_bootstraps: int = 50, n_random_unitaries: int = 100, n_median_of_means: int = 1)#

Bases: BenchmarkConfigurationBase

Graph States Benchmark configuration.

Parameters:
benchmark#

GraphStateBenchmark.

Type:

type[iqm.benchmarks.benchmark_definition.Benchmark]

qubits#

Physical qubit layout in which to benchmark graph state generation.

Type:

collections.abc.Sequence[int]

tomography#

Whether to use state or shadow tomography.

Type:

iqm.benchmarks.entanglement.graph_states.TomographyType

num_bootstraps#

Amount of bootstrap samples to use with state tomography.

Type:

int

n_random_unitaries#

Number of Haar random single-qubit unitaries to use for (local) shadow tomography.

Type:

int

n_median_of_means#

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

routing_method

physical_layout

use_dd

dd_strategy

active_reset_cycles

Methods

model_config = {}#

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

Contents