Quantum-classical hybrid is the use of both classical and quantum resources to solve problems, exploiting the complementary strengths that each provides. For an overview of, and motivation for, hybrid computing, see: Medium Article on Hybrid Computing.
Ocean software currently supports two types of hybrid solvers:
Leap’s Hybrid Solvers are cloud-based hybrid compute resources.
dwave-hybrid Hybrid Solvers are hybrid solvers developed in Ocean’s dwave-hybrid tool.
Leap’s Hybrid Solvers¶
D-Wave’s Leap quantum cloud service provides cloud-based hybrid solvers to which you can submit arbitrary quadratic models. 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.
Structural Imbalance in a Social Network is an example of submitting a problem for solution on a Leap hybrid solver.
dwave-hybrid Hybrid Solvers¶
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.
Large Map Coloring and Problem With Many Variables are examples of solving problems using dwave-hybrid samplers.