Simulators

Solvers

Model	Solvers
Quadratic Unconstrained Binary Optimization (QUBO)	sim.SimulatedAnnealingSampler, sim.RandomSampler

Simulated Annealing

A simple simulated annealing sampler for testing and debugging code.

dimod Simulated Annealing Sampler 🔗

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import DwaveSamplerParameterModel
from dimod import BinaryQuadraticModel

sw.authenticate(api_key)

linear = {1: -2, 2: -2, 3: -3, 4: -3, 5: -2}
quadratic = {(1, 2): 2, (1, 3): 2, (2, 4): 2, (3, 4): 2, (3, 5): 2, (4, 5): 2}
model = BinaryQuadraticModel(linear, quadratic, "BINARY")

solver = "sim.dimod_simulated_annealing_sampler"

options = DwaveSamplerParameterModel(num_reads=1000)
so = StrangeworksOptimizer(model=model, solver=solver, options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

print(f"Best solution:\n{results.solution.first}")

Random Sampler

A sampler that gives random samples for testing.

dimod Random Sampler 🔗

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import DwaveSamplerParameterModel
from dimod import BinaryQuadraticModel

sw.authenticate(api_key)

linear = {1: -2, 2: -2, 3: -3, 4: -3, 5: -2}
quadratic = {(1, 2): 2, (1, 3): 2, (2, 4): 2, (3, 4): 2, (3, 5): 2, (4, 5): 2}
model = BinaryQuadraticModel(linear, quadratic, "BINARY")

solver = "sim.dimod_random_sampler"

options = DwaveSamplerParameterModel(num_reads=1000)
so = StrangeworksOptimizer(model=model, solver=solver, options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

print(f"Best solution:\n{results.solution.first}")

Parameters

from strangeworks_optimization_models.parameter_models import DwaveSamplerParameterModel

options = DwaveSamplerParameterModel(.....)

Solvers

• dwave.Advantage_system4.1
• dwave.Advantage_system5.4
• dwave.Advantage_system6.4
• dwave.Advantage2_prototype2.6
• sim.SimulatedAnnealingSampler
• sim.RandomSampler

Name	Type	Description	Default	Values (Range)
num_reads	int	Specifies the number of reads (solutions) to be obtained.	1	[1, max_num_reads]
chain_strength	int	Sets the strength of the chains in the embedding.	Determined by solver	[Depends on system]
anneal_offsets	List[float]	Provides offsets to annealing paths, per qubit.	No offsets	Specified per qubit
anneal_schedule	List[List[float]]	Specifies the custom annealing schedule as time and fraction.	Standard schedule	Specified per system
annealing_time	float	Sets the duration of the annealing process per read.	Default system value	Specified per system
auto_scale	bool	Automatically rescales h and J values to the QPU's range.	True	[True, False]
flux_biases	List[float]	Applies manual flux biases for qubit calibration.	No biases	[Depends on system]
flux_drift_compensation	bool	Compensates for flux drift in qubits.	True	[True, False]
h_gain_schedule	List[List[float]]	Sets time-dependent gains for qubit biases.	Not specified	[Depends on system]
initial_state	dict	Sets the initial state for reverse annealing.	Not specified	[Depends on system]
max_answers	int	Limits the number of answers returned.	num_reads	[1, num_reads]
num_spin_reversal_transforms	int	Applies spin reversal transforms to reduce noise.	0	[0, max_transforms]
programming_thermalization	float	Time to wait post-programming for thermalization.	1000 μs	[0μs, max_thermal]
readout_thermalization	float	Time to wait post-readout for thermalization.	0 μs	[0μs, max_thermal]
reduce_intersample_correlation	bool	Adds delays between samples to reduce correlations.	False	[True, False]
reinitialize_state	bool	Reinitializes to the initial state for each anneal cycle.	False	[True, False]
embedding_parameters	dict	Specifies the embedding parameters.	False	[True, False]