Try out the new Hybrid Computing Jupyter Notebook, which demonstrates how you can apply dwave-hybrid solvers to your problem, create hybrid workflows, and develop custom hybrid components.
Quantum-classical hybrid is the use of both classical and quantum resources to solve problems, exploiting the complementary strengths that each provides. As quantum processors grow in size, offloading hard optimization problems to quantum computers promises performance benefits similar to CPUs' outsourcing of compute-intensive graphics-display processing to GPUs.
The dwave-hybrid framework enables rapid development of experimental prototypes, which provide insight into expected performance of the productized versions. It provides reference samplers and workflows you can quickly plug into your application code. You can easily experiment with customizing workflows that best solve your problem. You can also develop your own hybrid components to optimize performance.
Access this and other notebooks here.