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.
D-Wave offers two complementary approaches to hybrid computing:
- Through Leap, you can access the hybrid solver service (HSS): cloud-based hybrid solvers to which you can submit BQM, DQM, and CQM problems. 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, part of the Ocean SDK, provides a Python framework for building a variety of flexible hybrid workflows. 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 and examples, check out the documentation as well as our Hybrid Computing Jupyter Notebook.
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