Solver Properties & Parameters#
Large optimization problems can be formulated as nonlinear models, as described in the Model Construction section. You can then use the hybrid nonlinear solver (also known as the Stride™ hybrid solver) hosted in the Leap™ service to find good solutions.
For the properties and parameters of QPU solvers, see the QPU Solver Properties and QPU Solver Parameters sections.
For the properties and parameters of constrained quadratic models and other hybrid solvers, see the Other Solver Properties & Parameters section.
Properties of the nonlinear solver.
Parameters of the nonlinear solver.
Examples#
The following code solves an illustrative traveling-salesperson problem using a quantum-classical hybrid solver in the Leap service.
>>> from dwave.optimization.generators import traveling_salesperson
>>> from dwave.system import LeapHybridNLSampler
...
>>> DISTANCE_MATRIX = [
... [0, 656, 227, 578, 489],
... [656, 0, 889, 141, 170],
... [227, 889, 0, 773, 705],
... [578, 141, 773, 0, 161],
... [489, 170, 705, 161, 0]]
...
>>> model = traveling_salesperson(distance_matrix=DISTANCE_MATRIX)
>>> with LeapHybridNLSampler() as sampler:
... results = sampler.sample(
... model,
... label='SDK Examples - TSP')