| Name | cudaq-solvers JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | Accelerated libraries for quantum-classical solvers built on CUDA-Q |
| upload_time | 2025-08-01 20:39:43 |
| maintainer | NVIDIA Corporation & Affiliates |
| docs_url | None |
| author | NVIDIA Corporation & Affiliates |
| requires_python | >=3.10 |
| license | None |
| keywords |
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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](https://nvidia.github.io/cudaqx/components/solvers/introduction.html).
## License
CUDA-Q Solvers is an open source project. The source code is available on
[GitHub][github_link] and licensed under [Apache License
2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).
[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/solvers
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"description": "# CUDA-Q Solvers Library\n\nCUDA-Q Solvers provides GPU-accelerated implementations of common\nquantum-classical hybrid algorithms and numerical routines frequently\nused in quantum computing applications. The library is designed to\nwork seamlessly with CUDA-Q quantum programs.\n\n**Note**: CUDA-Q Solvers is currently only supported on Linux operating systems\nusing `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q Solvers does\nnot require a GPU to use, but some components are GPU-accelerated.\n\n**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`.\n\n## Features\n\n- Variational quantum eigensolvers (VQE)\n- ADAPT-VQE\n- Quantum approximate optimization algorithm (QAOA)\n- Hamiltonian simulation routines\n\nNote: if you would like to use our Generative Quantum Eigensolver API, you will need\nadditional dependencies installed. You can install them with\n`pip install cudaq-solvers[gqe]`.\n\n## Getting Started\n\nFor detailed documentation, tutorials, and API reference,\nvisit the [CUDA-Q Solvers Documentation](https://nvidia.github.io/cudaqx/components/solvers/introduction.html).\n\n## License\n\nCUDA-Q Solvers is an open source project. The source code is available on\n[GitHub][github_link] and licensed under [Apache License\n2.0](https://github.com/NVIDIA/cudaqx/blob/main/LICENSE).\n\n[github_link]: https://github.com/NVIDIA/cudaqx/tree/main/libs/solvers\n",
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