D-Wave

D-Wave's quantum annealing hardware relies on metal loops of niobium that have tiny electrical currents running through them.

Solvers

We offer access to D-Wave QPU Samplers, classical simulators, and D-Wave Leap Hybrid.

Model	Backends (see catalog for complete list)
BinaryQuadraticModel	dwave.Advantage_system6.4, dwave.Advantage2_prototype2.3, dwave.hybrid_binary_quadratic_model_version2p
ConstrainedQuadraticModel	dwave.hybrid_constrained_quadratic_model_version1p
DiscreteQuadraticModel	dwave.hybrid_discrete_quadratic_model_version1p

Samplers

D-Wave samplers are designed to solve problems that can be formulated as binary quadratic models (BQM). The samplers are used to find the lowest energy state of the BQM.

Advantage QPUs

• Advantage_system6.4
• Advantage2_prototype2.3

https://www.dwavesys.com/solutions-and-products/systems/

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

sw.authenticate('your-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 = "dwave.Advantage_system6.4"
solver = "dwave.Advantage2_prototype2.3"
options = DwaveSamplerParameterModel(num_reads=100, chain_strength=50)

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}")

Leap Hybrid Solvers

• hybrid_binary_quadratic_model_version2p
• hybrid_constrained_quadratic_model_version1p
• hybrid_discrete_quadratic_model_version1p

https://www.dwavesys.com/solutions-and-products/cloud-platform/

BinaryQuadraticModel

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

sw.authenticate('your-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 = "dwave.hybrid_binary_quadratic_model_version2p"

so = StrangeworksOptimizer(model=model, solver=solver)
sw_job = so.run()

ConstrainedQuadraticModel

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import DwaveSamplerParameterModel
from dimod import Binary, ConstrainedQuadraticModel

sw.authenticate('your-api-key')

weights = [0.9, 0.7, 0.2, 0.1]
capacity = 1
y = [Binary(f"y_{j}") for j in range(len(weights))]
x = [[Binary(f"x_{i}_{j}") for j in range(len(weights))] for i in range(len(weights))]
model = ConstrainedQuadraticModel()
model.set_objective(sum(y))

for i in range(len(weights)):
    model.add_constraint(sum(x[i]) == 1, label=f"item_placing_{i}")

for j in range(len(weights)):
    model.add_constraint(
        sum(weights[i] * x[i][j] for i in range(len(weights))) - y[j] * capacity <= 0,
        label=f"capacity_bin_{j}",
    )

solver = "dwave.hybrid_constrained_quadratic_model_version1p"

so = StrangeworksOptimizer(model=model, solver=solver)
sw_job = so.run()

DiscreteQuadraticModel

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

sw.authenticate('your-api-key')

model = DiscreteQuadraticModel()
for i in range(10):
    model.add_variable(4)
for i in range(9):
    for j in range(i + 1, 10):
        model.set_quadratic_case(i, random.randrange(0, 4), j, random.randrange(0, 4), random.random())

solver = "dwave.hybrid_discrete_quadratic_model_version1p"

so = StrangeworksOptimizer(model=model, solver=solver)
sw_job = so.run()

DwaveSamplerParameterModel

For more information on the parameters, please refer to the D-Wave Solver Parameters documentation.

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]