iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRBConfiguration

iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRBConfiguration#

class iqm.benchmarks.randomized_benchmarking.clifford_rb.clifford_rb.CliffordRBConfiguration(*, benchmark: 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, 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_array: ~collections.abc.Sequence[~collections.abc.Sequence[int]], sequence_lengths: ~collections.abc.Sequence[int], num_circuit_samples: int, parallel_execution: bool = False)#

Bases: BenchmarkConfigurationBase

Clifford RB configuration.

Parameters:
benchmark#

CliffordRandomizedBenchmarking

Type:

type[iqm.benchmarks.benchmark_definition.Benchmark]

qubits_array#

The array of qubits on which to execute the benchmark.

Type:

collections.abc.Sequence[collections.abc.Sequence[int]]

sequence_lengths#

The length of Cliffords sequences with which to execute benchmark.

Type:

collections.abc.Sequence[int]

num_circuit_samples#

The number of Cliffords circuits per sequence length.

Type:

int

shots#

The number of measurement shots per circuit.

parallel_execution#

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

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

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].

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