Industrial Optimization#
The Industrial Optimization section shows how to optimize business problems using the Leap™ service’s quantum-classical hybrid solvers.
Learn about optimizing with hybrid solvers.
How D‑Wave and other companies develop successful quantum applications.
Properties and parameters of hybrid solvers in the Leap service.
Scaling and usage best-practices.
Performance studies on use cases.
The Quantum Research section shows how to use D‑Wave™ quantum processing units (QPU) directly.
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)
>>> with LeapHybridNLSampler() as sampler:
... results = sampler.sample(
... model,
... label='SDK Examples - TSP')
Useful Links#
Configuring Access to the Leap Service (Basic) on accessing the hybrid solvers in the Leap service.
Solver Usage Charges on how the Leap service charges your account for use.
Scaling for Production on coding for production applications.