Improving Hybrid Solutions

Learn best practices for using the Leap service’s hybrid solvers in this section.

Best Practices

Simple guidance for using the hybrid solvers.

Scaling for Production

Coding your application for performance on problems of the industrial scale.

Hybrid Usage Examples

Advanced end-to-end examples.

Example

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)
>>> sampler = LeapHybridNLSampler()     
>>> results = sampler.sample(
...     model,
...     label='SDK Examples - TSP')