Industrial Optimization#

The Industrial Optimization section shows how to optimize business problems using the Leap™ service’s quantum-classical hybrid solvers.

Get Started with Optimization

Learn about optimizing with hybrid solvers.

Get Started with Optimization
Developing Quantum Applications

How D-Wave and other companies develop successful quantum applications.

Developing Quantum Applications
Solver Properties & Parameters

Properties and parameters of hybrid solvers in the Leap service.

Solver Properties & Parameters
Improving Hybrid Solutions

Scaling and usage best-practices.

Improving Hybrid Solutions
Performance Benchmarks

Performance studies on use cases.

Performance Benchmarks

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