Skip to content

mis.pipeline.config

source module mis.pipeline.config

Configuration for MIS solvers.

Classes

  • BackendConfig — Generic configuration for backends.

  • BackendType — Type of backend to use for solving the QUBO.

  • SolverConfig — Configuration class for setting up solver parameters.

  • GreedyConfig — Configuration for greedy solving strategies.

Functions

class BackendConfig(**data: Any)

Bases : pydantic.BaseModel

Generic configuration for backends.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Attributes

  • model_config : ClassVar[ConfigDict] — Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

  • model_extra : dict[str, Any] | None — Get extra fields set during validation.

  • model_fields_set : set[str] — Returns the set of fields that have been explicitly set on this model instance.

  • backend : BackendType — The type of backend to use.

  • username : str | None — For a backend that requires authentication, such as Pasqal Cloud,.

  • password : str | None — For a backend that requires authentication, such as Pasqal Cloud,.

  • project_id : str | None — For a backend that associates jobs to projects, such as Pasqal Cloud,.

  • device : NamedDevice | DeviceType | None

  • dt : int — For a backend that supports customizing the duration of steps, the.

enum BackendType()

Bases : StrEnum

Type of backend to use for solving the QUBO.

Attributes

  • QUTIP

  • REMOTE_QPU

  • REMOTE_EMUMPS

  • EMU_MPS

  • EMU_SV

source dataclass SolverConfig(backend: BaseBackend | BackendConfig | None = None, method: MethodType = MethodType.EAGER, max_iterations: int = 1, max_number_of_solutions: int = 1, device: Device | None = None, embedder: BaseEmbedder | None = None, pulse_shaper: BasePulseShaper | None = None, preprocessor: Callable[[nx.Graph], BasePreprocessor] | None = field(default_factory=default_preprocessor), postprocessor: Callable[[], BasePostprocessor] | None = default_postprocessor, greedy: GreedyConfig = field(default_factory=GreedyConfig), runs: int = 500)

Configuration class for setting up solver parameters.

Attributes

  • backend : BaseBackend | BackendConfig | None — Backend configuration to use. If None, use a non-quantum heuristic solver.

  • method : MethodType — The method used to solve this instance of MIS.

  • max_iterations : int — Maximum number of iterations allowed for solving.

  • max_number_of_solutions : int — A maximal number of solutions to return.

  • device : Device | None — Quantum device to execute the code in. If unspecified, use a reasonable default device.

  • embedder : BaseEmbedder | None — If specified, an embedder, i.e. a mechanism used to customize the layout of neutral atoms on the quantum device. Ignored for non-quantum backends.

  • pulse_shaper : BasePulseShaper | None — If specified, a pulse shaper, i.e. a mechanism used to customize the laser pulse to which the neutral atoms are subjected during the execution of the quantum algorithm. Ignored for non-quantum backends.

  • preprocessor : Callable[[nx.Graph], BasePreprocessor] | None — A graph preprocessor, used to decrease the size of the graph (hence the duration of actual resolution) by applying heuristics prior to embedding on a quantum device.

  • postprocessor : Callable[[], BasePostprocessor] | None — A postprocessor used to sort out and improve results.

  • greedy : GreedyConfig — If specified, use this for solving the GreedyMIS. Needs to be specified when method is GreedyMIS

  • runs : int — When using a quantum backend, how many times we repeat each quantum run. Since quantum executions are non-deterministic, higher numbers increases the reliability of the result, but also the duration until the results are available.

source default_preprocessor() → Callable[[nx.Graph], BasePreprocessor]

Instantiate the default preprocessor.

As of this writing, the default preprocessor is mis.pipeline.kernelization.Kernelization.

source default_postprocessor() → BasePostprocessor

Instantiate the default postprocessor.

As of this writing, the default postprocessor is mis.pipeline.maximization.Maximization.

source dataclass GreedyConfig(default_solving_threshold: int = 2, subgraph_quantity: int = 5, mis_sample_quantity: int = 1)

Configuration for greedy solving strategies.

Attributes

  • default_solving_threshold : int — Size threshold (number of nodes) for using MIS solving when greedy method is used.

  • subgraph_quantity : int — Number of candidate subgraphs to generate during greedy mapping.

  • mis_sample_quantity : int — Number of MIS solutions to sample per iteration (if applicable).