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.
For an overview of, and motivation for, hybrid computing, see this Medium Article.
D-Wave’s Leap quantum cloud service provides cloud-based hybrid solvers you can submit arbitrary BQMs to. These solvers, which implement state-of-the-art classical algorithms together with intelligent allocation of the quantum processing unit (QPU) to parts of the problem where it benefits most, are designed to accommodate even very large problems. Leap’s solvers can relieve you of the burden of any current and future development and optimization of hybrid algorithms that best solve your problem.
dwave-hybrid provides you with a Python framework for building a variety of flexible hybrid workflows. These use quantum and classical resources together to find good solutions to your problem. For example, a hybrid workflow might use classical resources to find a problem’s hard core and send that to the QPU, or break a large problem into smaller pieces that can be solved on a QPU and then recombined.
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.
For more information on hybrid computing, see the following:
Describes how to use reference hybrid solvers, build hybrid workflows, and your own hybrid components.
Introduces Leap‘s quantum-classical hybrid solvers and provides references to usage information.
Getting Started Demonstrations and Jupyter Notebooks
Provides pointers to a code-examples repository and Jupyter Notebooks, which have relevant content.