These notebooks demonstrate how you can apply hybrid solvers to your problem, create hybrid workflows, and develop custom hybrid components.
- Notebook 01 will start you off with Leap's cloud-based hybrid solvers and out-of-the-box dwave-hybrid samplers and workflows, and requires only familiarity with the binary quadratic model (BQM) approach to formulating problems for solution on quantum computers.
- Notebook 02 shows how you create your own workflows using existing dwave-hybrid components.
- Notebook 03 is aimed at developers interested in developing their own hybrid components for optimal performance.
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.
This Medium Article provides an overview of, and motivation for, hybrid computing.
D-Wave's Leap quantum cloud service provides cloud-based hybrid solvers you can submit arbitrary quadratic models 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.
You can run this example without installation in cloud-based IDEs that support the Development Containers specification (aka "devcontainers").
For development environments that do not support devcontainers, install
requirements:
pip install -r requirements.txt
If you are cloning the repo to your local system, working in a virtual environment is recommended.
Your development environment should be configured to access Leap’s Solvers. You can see information about supported IDEs and authorizing access to your Leap account here.
The notebooks can be opened by clicking on the .ipynb files
(e.g., 01-hybrid-computing-getting-started.ipynb) in VS Code-based IDEs.
To run a locally installed notebook:
jupyter notebookReleased under the Apache License 2.0. See LICENSE file.