CUDA-QX - The CUDA-Q Libraries Collection

CUDA-QX is a collection of libraries that build upon the CUDA-Q programming model to enable the rapid development of hybrid quantum-classical application code leveraging state-of-the-art CPUs, GPUs, and QPUs. It provides C++ libraries and Python packages that enable research, development, and application creation for use cases in quantum error correction.

Note

CUDA-Q QEC is actively developed and fully supported. The deprecation notice below applies only to the CUDA-Q Solvers library; it does not affect CUDA-Q QEC, which continues to receive new features, performance improvements, and releases.

Attention

CUDA-Q Solvers is deprecated (this does not affect CUDA-Q QEC). Version 0.6.0 is the final planned release of the CUDA-Q Solvers library. Development continues in CUDA-Q Algorithms, which supersedes CUDA-Q Solvers and expands on it. Install it with pip install cudaq-algorithms (cudaq-algorithms on PyPI), read the CUDA-Q Algorithms documentation, and find the source code at NVIDIA/cudaq-algorithms on GitHub.

Getting Started

Libraries

Examples

Performance Studies

API Reference

Key Features

CUDA-QX provides cudaq-qec, a library enabling performant research workflows for quantum error correction, built upon the CUDA-Q programming model.

  • cudaq-qec (actively developed and supported): Quantum Error Correction Library

    • Extensible framework describing quantum error correcting codes as a collection of CUDA-Q kernels.

    • Extensible framework for describing syndrome decoders

    • State-of-the-art, performant decoder implementations on NVIDIA GPUs

    • Real-time decoding for active error correction on quantum hardware

    • Pre-built numerical experiment APIs

Indices