CUDA-Q Solvers Library

CUDA-Q Solvers provides GPU-accelerated implementations of common quantum-classical hybrid algorithms and numerical routines frequently used in quantum computing applications. The library is designed to work seamlessly with CUDA-Q quantum programs.

Note: CUDA-Q Solvers is currently only supported on Linux operating systems using x86_64 processors or aarch64/arm64 processors. CUDA-Q Solvers does not require a GPU to use, but some components are GPU-accelerated.

Note: CUDA-Q Solvers will require the presence of libgfortran, which is not distributed with the Python wheel, for provided classical optimizers. If libgfortran is not installed, you will need to install it via your distribution's package manager. On debian based systems, you can install this with apt-get install gfortran.

Features

• Variational quantum eigensolvers (VQE)
• ADAPT-VQE
• Quantum approximate optimization algorithm (QAOA)
• Hamiltonian simulation routines

Note: if you would like to use our Generative Quantum Eigensolver API, you will need additional dependencies installed. You can install them with pip install cudaq-solvers[gqe].

Getting Started

For detailed documentation, tutorials, and API reference, visit the CUDA-Q Solvers Documentation.

License

CUDA-Q Solvers is an open source project. The source code is available on GitHub and licensed under Apache License 2.0.