PopulationAnnealingQpu¶
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.
Note
This solver is only available for commercial and academic licenses.
Compatible Backends¶
The PopulationAnnealingQpu algorithm supports the following backends:
DWaveQpuBy default,PopulationAnnealingQpuuses theDWaveQpubackend.
Initialization¶
The following section outlines the default configurations of PopulationAnnealingQpu. 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.
from luna_quantum.algorithms import PopulationAnnealingQpu
from luna_quantum.solve.parameters.algorithms.base_params import (
Decomposer,
QuantumAnnealingParams
)
algorithm = PopulationAnnealingQpu(
backend=None,
num_reads=100,
num_retries=0,
max_iter=20,
max_time=2,
fixed_temp_sampler_num_sweeps=10000,
fixed_temp_sampler_num_reads=None,
decomposer=Decomposer(
size=10,
min_gain=None,
rolling=True,
rolling_history=1.0,
silent_rewind=True,
traversal='energy'
),
quantum_annealing_params=QuantumAnnealingParams(
anneal_offsets=None,
anneal_schedule=None,
annealing_time=None,
auto_scale=None,
fast_anneal=False,
flux_biases=None,
flux_drift_compensation=True,
h_gain_schedule=None,
initial_state=None,
max_answers=None,
num_reads=1,
programming_thermalization=None,
readout_thermalization=None,
reduce_intersample_correlation=False,
reinitialize_state=None
)
)
Parameter Details
For a complete overview of available parameters and their usage, see the PopulationAnnealingQpu API Reference.
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")
API Reference¶
Bases: LunaAlgorithm[DWaveQpu]
Parameters for the Population Annealing QPU 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 |
|---|---|---|
num_reads |
int
|
Number of annealing cycles to perform on the D-Wave QPU. Default is 100. |
num_retries |
int
|
Number of attempts to retry embedding the problem onto the quantum hardware. Default is 0. |
max_iter |
int
|
Maximum number of iterations. Controls how many rounds of annealing and population adjustments are performed. Default is 20. |
max_time |
int
|
Maximum time in seconds that the algorithm is allowed to run. Serves as a stopping criterion alongside max_iter. Default is 2. |
fixed_temp_sampler_num_sweeps |
int
|
Number of Monte Carlo sweeps to perform, where one sweep attempts to update all variables once. More sweeps produce better equilibrated samples but increase computation time. 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. Each run produces one sample from the equilibrium distribution. Multiple reads provide better statistical coverage of the solution space. Default is None, which typically defaults to 1 or matches the number of initial states provided. |
decomposer |
Decomposer
|
Decomposer: Breaks down problems into subproblems of manageable size Default is a Decomposer instance with default settings. |
quantum_annealing_params |
QuantumAnnealingParams
|
Parameters that control the quantum annealing process, including annealing schedule, temperature settings, and other quantum-specific parameters. These settings determine how the system transitions from quantum superposition to classical states during the optimization process. |
backend
class-attribute
instance-attribute
¶
decomposer
class-attribute
instance-attribute
¶
decomposer: Decomposer = Field(default_factory=Decomposer)
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
model_config
class-attribute
instance-attribute
¶
quantum_annealing_params
class-attribute
instance-attribute
¶
quantum_annealing_params: QuantumAnnealingParams = Field(
default_factory=QuantumAnnealingParams
)
get_compatible_backends
classmethod
¶
get_default_backend
classmethod
¶
get_default_backend() -> DWaveQpu
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. |
Bases: BaseModel
Configuration for breaking down larger problems into subproblems for DWave QPUs.
Attributes:
| Name | Type | Description |
|---|---|---|
size |
int, default=10
|
Nominal number of variables in each subproblem. The actual subproblem can be
smaller depending on other parameters (e.g., |
min_gain |
Optional[float], default=None
|
Minimum required energy reduction threshold for including a variable in the subproblem. A variable is included only if flipping its value reduces the BQM energy by at least this amount. If None, no minimum gain is required. |
rolling |
bool, default=True
|
Controls variable selection strategy for successive calls on the same problem:
|
rolling_history |
float, default=1.0
|
Fraction of the problem size (range 0.0 to 1.0) that participates in the rolling selection. Once this fraction of variables has been processed, subproblem unrolling is reset. Min: 0.0, Max: 1.0 |
silent_rewind |
bool, default=True
|
Controls behavior when resetting/rewinding the subproblem generator:
|
traversal |
Literal["energy", "bfs", "pfs"], default="energy"
|
Algorithm used to select a subproblem of
|
Bases: BaseModel
Parameters for quantum annealing sampling on physical quantum processors (QPUs).
These parameters control the quantum annealing process on hardware devices like D-Wave quantum annealers, specifying how the annealing is performed, how many samples to collect, and various hardware-specific settings that affect solution quality and runtime.
Attributes:
| Name | Type | Description |
|---|---|---|
anneal_offsets |
list[float] | None
|
Per-qubit time offsets for the annealing path in normalized annealing time units. List of floats with length equal to the number of qubits. Default is None. |
anneal_schedule |
list[tuple[float, float]] | None
|
Custom schedule for the annealing process as a list of (time, s) pairs. Time is in normalized units [0, 1] and s is the annealing parameter [0, 1]. Default is None. |
annealing_time |
float | None
|
Duration of the annealing process in microseconds. Must be within the range supported by the QPU hardware. Default is None. |
auto_scale |
bool | None
|
Whether to automatically normalize the problem energy range to use the full range of h and J values supported by the hardware. Default is None. |
fast_anneal |
bool
|
Use accelerated annealing protocol for shorter annealing times. Default is False. |
flux_biases |
list[float] | None
|
Custom flux bias offsets for each qubit in units of Ξ¦β (flux quantum). List length must equal the number of qubits. Default is None. |
flux_drift_compensation |
bool
|
Whether to compensate for drift in qubit flux over time using real-time calibration data. Default is True. |
h_gain_schedule |
list[tuple[float, float]] | None
|
Schedule for h-gain during annealing as a list of (time, gain) pairs. Time is in normalized units [0, 1]. Default is None. |
initial_state |
list[int] | None
|
Starting state for the annealing process. List of {-1, +1} values with length equal to the number of qubits. Default is None. |
max_answers |
int | None
|
Maximum number of unique answer states to return. Must be β€ num_reads. Default is None. |
num_reads |
int
|
Number of annealing cycles to perform. Must be positive integer. Default is 1. |
programming_thermalization |
float | None
|
Wait time after programming the QPU in microseconds to allow the system to thermalize. Default is None. |
readout_thermalization |
float | None
|
Wait time after each anneal before reading results in microseconds. Default is None. |
reduce_intersample_correlation |
bool
|
Whether to add delay between samples to reduce correlation between consecutive measurements. Default is False. |
reinitialize_state |
bool | None
|
Whether to reset to a new initial state between reads to reduce correlation. Default is None. |
anneal_schedule
class-attribute
instance-attribute
¶
annealing_time
class-attribute
instance-attribute
¶
annealing_time: float | None = Field(default=None, gt=0)
h_gain_schedule
class-attribute
instance-attribute
¶
max_answers
class-attribute
instance-attribute
¶
max_answers: int | None = Field(default=None, ge=1)
programming_thermalization
class-attribute
instance-attribute
¶
programming_thermalization: float | None = Field(default=None, gt=0)
readout_thermalization
class-attribute
instance-attribute
¶
readout_thermalization: float | None = Field(default=None, gt=0)