| Name | cudaq-qec JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | Accelerated libraries for Quantum Error Correction built on CUDA-Q |
| upload_time | 2025-08-01 20:40:39 |
| maintainer | NVIDIA Corporation & Affiliates |
| docs_url | None |
| author | NVIDIA Corporation & Affiliates |
| requires_python | >=3.10 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
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| requirements |
No requirements were recorded.
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No Travis.
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| coveralls test coverage |
No coveralls.
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# CUDA-Q QEC Library
CUDA-Q QEC is a high-performance quantum error correction library
that leverages NVIDIA GPUs to accelerate classical decoding and
processing of quantum error correction codes. The library provides optimized
implementations of common QEC tasks including syndrome extraction,
decoding, and logical operation tracking.
**Note**: CUDA-Q QEC is currently only supported on Linux operating systems
using `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q QEC does
not require a GPU to use, but some components are GPU-accelerated.
## Features
- Fast syndrome extraction and processing on GPUs
- Common decoders for surface codes and other topological codes
- Real-time decoding capabilities for quantum feedback
- Integration with CUDA-Q quantum program execution
Note: if you would like to use our Tensor Network Decoder, you will need
additional dependencies installed. You can install them with
`pip install cudaq-qec[tensor_network_decoder]`.
## Getting Started
For detailed documentation, tutorials, and API reference, visit the
[CUDA-Q QEC Documentation](https://nvidia.github.io/cudaqx/components/qec/introduction.html).
## License
CUDA-Q QEC 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/qec
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"description": "# CUDA-Q QEC Library\n\nCUDA-Q QEC is a high-performance quantum error correction library\nthat leverages NVIDIA GPUs to accelerate classical decoding and\nprocessing of quantum error correction codes. The library provides optimized\nimplementations of common QEC tasks including syndrome extraction,\ndecoding, and logical operation tracking.\n\n**Note**: CUDA-Q QEC is currently only supported on Linux operating systems\nusing `x86_64` processors or `aarch64`/`arm64` processors. CUDA-Q QEC does\nnot require a GPU to use, but some components are GPU-accelerated.\n\n## Features\n\n- Fast syndrome extraction and processing on GPUs\n- Common decoders for surface codes and other topological codes\n- Real-time decoding capabilities for quantum feedback\n- Integration with CUDA-Q quantum program execution\n\nNote: if you would like to use our Tensor Network Decoder, you will need\nadditional dependencies installed. You can install them with\n`pip install cudaq-qec[tensor_network_decoder]`.\n\n## Getting Started\n\nFor detailed documentation, tutorials, and API reference, visit the\n[CUDA-Q QEC Documentation](https://nvidia.github.io/cudaqx/components/qec/introduction.html).\n\n## License\n\nCUDA-Q QEC 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/qec\n",
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