PopulationAnnealing

Population Annealing uses a sequential Monte Carlo method to minimize the energy of a population. The population consists of walkers that can explore their neighborhood during the cooling process. Afterwards, walkers are removed and duplicated using bias to lower energy. Eventually, a population collapse occurs where all walkers are in the lowest energy state.

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Compatible Backends

The PopulationAnnealing algorithm supports the following backends:

• DWave

By default, PopulationAnnealing uses the DWave backend.

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Initialization

The following section outlines the default configurations of PopulationAnnealing. You can also specify other compatible backends for the algorithm. When backend=None is specified, the default backend will be initialized automatically. In this case, if the backend requires a token, it will be taken from the environment variables.

Default Configuration

from luna_quantum.solve.parameters.algorithms import PopulationAnnealing

algorithm = PopulationAnnealing(
    backend=None,
    max_iter=20,
    max_time=2,
    fixed_temp_sampler_num_sweeps=10000,
    fixed_temp_sampler_num_reads=None
)

Parameter Details

For a complete overview of available parameters and their usage, see the PopulationAnnealing API Reference.

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Usage

from luna_quantum import LunaSolve

LunaSolve.authenticate("<YOUR_LUNA_API_KEY>")

# Define your model and algorithm
model = ...
algorithm = ...

solve_job = algorithm.run(model, name="my-solve-job")

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API Reference

Bases: LunaAlgorithm[DWave]

Parameters for the Population Annealing algorithm.

Population Annealing uses a sequential Monte Carlo method to minimize the energy of a population. The population consists of walkers that can explore their neighborhood during the cooling process. Afterwards, walkers are removed and duplicated using bias to lower energy. Eventually, a population collapse occurs where all walkers are in the lowest energy state.

Attributes:

Name	Type	Description
max_iter	int	Maximum number of annealing iterations (temperature steps) to perform. Each iteration involves lowering the temperature, allowing walkers to explore locally, and then resampling the population based on Boltzmann weights. Higher values allow for a more gradual cooling schedule, potentially finding better solutions but increasing computation time. Default is 20, which provides a reasonable balance for most problems.
max_time	int	Maximum time in seconds that the algorithm is allowed to run. Provides a hard time limit regardless of convergence or iteration status. Useful for time-constrained scenarios where some solution is needed within a specific timeframe. Default is 2, which is relatively aggressive and may need to be increased for complex problems.
fixed_temp_sampler_num_sweeps	int	Number of Monte Carlo sweeps to perform at each temperature level, where one sweep attempts to update all variables once. More sweeps allow walkers to explore their local configuration space more thoroughly, producing better equilibrated samples but increasing computation time. This parameter directly affects how well each walker samples its local energy landscape before resampling occurs. Default is 10,000, which is suitable for thorough exploration of moderate-sized problems.
fixed_temp_sampler_num_reads	int | None	Number of independent sampling runs to perform at each temperature level. Each run effectively initializes a separate walker in the population. Multiple reads provide better coverage of the solution space, increasing the diversity of the initial population and improving the chances of finding the global optimum. Default is None, which typically defaults to 1 or matches the number

backend class-attribute instance-attribute

backend: BACKEND_TYPE | None = Field(default=None, exclude=True, repr=False)

fixed_temp_sampler_num_reads class-attribute instance-attribute

fixed_temp_sampler_num_reads: int | None = None

fixed_temp_sampler_num_sweeps class-attribute instance-attribute

fixed_temp_sampler_num_sweeps: int = 10000

max_iter class-attribute instance-attribute

max_iter: int = 20

max_time class-attribute instance-attribute

max_time: int = 2

model_config class-attribute instance-attribute

model_config = ConfigDict(
    arbitrary_types_allowed=True, extra="allow", validate_assignment=True
)

get_compatible_backends classmethod

get_compatible_backends() -> tuple[type[DWave], ...]

Check at runtime if the used backend is compatible with the solver.

Returns:

Type	Description
tuple[type[IBackend], ...]	True if the backend is compatible with the solver, False otherwise.

get_default_backend classmethod

get_default_backend() -> DWave

Return the default backend implementation.

This property must be implemented by subclasses to provide the default backend instance to use when no specific backend is specified.

Returns:

Type	Description
IBackend	An instance of a class implementing the IBackend interface that serves as the default backend.

run

run(
    model: Model | str,
    name: str | None = None,
    backend: BACKEND_TYPE | None = None,
    client: LunaSolve | str | None = None,
    *args: Any,
    **kwargs: Any,
) -> SolveJob

Run the configured solver.

Parameters:

Name	Type	Description	Default
model	Model or str	The model to be optimized or solved. It could be an Model instance or a string identifier representing the model id.	required
name	str | None	If provided, the name of the job. Defaults to None.	None
backend	BACKEND_TYPE | None	Backend to use for the solver. If not provided, the default backend is used.	None
client	LunaSolve or str	The client interface used to interact with the backend services. If not provided, a default client will be used.	None
*args	Any	Additional arguments that will be passed to the solver or client.	()
**kwargs	Any	Additional keyword parameters for configuration or customization.	{}

Returns:

Type	Description
SolveJob	The job object containing the information about the solve process.