iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb.MirrorRBConfiguration

class iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb.MirrorRBConfiguration(*, benchmark: ~typing.Type[~iqm.benchmarks.benchmark_definition.Benchmark] = <class 'iqm.benchmarks.randomized_benchmarking.mirror_rb.mirror_rb.MirrorRandomizedBenchmarking'>, 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_array: ~typing.Sequence[~typing.Sequence[int]], depths_array: ~typing.Sequence[~typing.Sequence[int]], num_circuit_samples: int, num_pauli_samples: int, qiskit_optim_level: int = 1, two_qubit_gate_ensemble: ~typing.Dict[str, float] = {'CZGate': 1.0}, density_2q_gates: float = 0.25, clifford_sqg_probability: float = 1.0, sqg_gate_ensemble: ~typing.Dict[str, float] | None = None, simulation_method: ~typing.Literal['automatic', 'statevector', 'stabilizer', 'extended_stabilizer', 'matrix_product_state'] = 'automatic')

Bases: BenchmarkConfigurationBase

Mirror RB 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_array (Sequence[Sequence[int]]) –

• depths_array (Sequence[Sequence[int]]) –

• num_circuit_samples (int) –

• num_pauli_samples (int) –

• qiskit_optim_level (int) –

• two_qubit_gate_ensemble (Dict[str, float]) –

• density_2q_gates (float) –

• clifford_sqg_probability (float) –

• sqg_gate_ensemble (Dict[str, float] | None) –

• simulation_method (Literal['automatic', 'statevector', 'stabilizer', 'extended_stabilizer', 'matrix_product_state']) –

benchmark

MirrorRandomizedBenchmarking.

Type:

Type[Benchmark]

qubits_array

The array of physical qubits in which to execute MRB.

Type:

Sequence[Sequence[int]]

depths_array

The array of physical depths in which to execute MRB for a corresponding qubit list. * If len is the same as that of qubits_array, each Sequence[int] corresponds to the depths for the corresponding layout of qubits. * If len is different from that of qubits_array, assigns the first Sequence[int].

Type:

Sequence[Sequence[int]]

num_circuit_samples

The number of random-layer mirror circuits to generate.

Type:

int

num_pauli_samples

The number of random Pauli layers to interleave per mirror circuit.

Type:

int

shots

The number of measurement shots to execute per circuit.

Type:

int

qiskit_optim_level

The Qiskit-level of optimization to use in transpilation. * Default is 1.

Type:

int

routing_method

The routing method to use in transpilation. * Default is “sabre”.

Type:

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

two_qubit_gate_ensemble

The two-qubit gate ensemble to use in the random mirror circuits. * Keys correspond to str names of qiskit circuit library gates, e.g., “CZGate” or “CXGate”. * Values correspond to the probability for the respective gate to be sampled. * Default is {“CZGate”: 1.0}.

Type:

Dict[str, float]

density_2q_gates

The expected density of 2-qubit gates in the final circuits. * Default is 0.25.

Type:

float

clifford_sqg_probability

Probability with which to uniformly sample Clifford 1Q gates. * Default is 1.0.

Type:

float

sqg_gate_ensemble

A dictionary with keys being str specifying 1Q gates, and values being corresponding probabilities. * Default is None.

Type:

Optional[Dict[str, float]]

simulation_method

Qiskit’s Aer simulation method * Default is “automatic”.

Type:

Literal[“automatic”, “statevector”, “stabilizer”, “extended_stabilizer”, “matrix_product_state”]

Attributes

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
benchmark
qubits_array
depths_array
num_circuit_samples
num_pauli_samples
qiskit_optim_level
two_qubit_gate_ensemble
density_2q_gates
clifford_sqg_probability
sqg_gate_ensemble
simulation_method
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].