# Using Leap’s Hybrid Solvers¶

Introduces Leap‘s quantum-classical hybrid solvers and provides references to usage information.

Note

Not all accounts have access to this type of solver.

## Leap’s Hybrid Solvers¶

Leap’s quantum-classical hybrid solvers are intended to solve arbitrary application problems formulated as binary quadratic models (BQM).

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.

## Examples¶

Use a hybrid solver as you would any Ocean sampler. For example, given a 1000-variable problem formulated as a quadratic unconstrained binary optimization (QUBO) model, \(Q\), you can submit it for solution as follows:

```
from dwave.system import LeapHybridSampler
# Select a solver
sampler = LeapHybridSampler()
# Submit for solution
answer = sampler.sample_qubo(Q)
```

You can find more detailed usage examples here:

- Hybrid solver example in Ocean software’s Getting Started documentation.
- Leap‘s Hybrid Computing Jupyter Notebook and coding examples.

## Solver Information¶

D-Wave’s system documentation provides detailed solver information in:

*Solver Properties and Parameters Reference*details hybrid solver’s properties and parameters.*Solver Computation Time*details hybrid solver’s timing information.