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 a collection of C++ libraries and Python packages that enable research, development, and application creation for use cases in quantum error correction and hybrid quantum-classical solvers.

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. The CUDA-Q Solvers documentation below is retained for existing 0.6.0 users; all new features and fixes land in CUDA-Q Algorithms.

Getting Started

• Installation Guide
  • Installation Methods
  • Installing PyTorch

Libraries

• CUDA-Q QEC - Quantum Error Correction Library
• CUDA-Q Solvers Library

Examples

• CUDA-Q QEC by Example
  • Creating New QEC Codes
  • Experiments and Noise Modeling
  • Decoders
  • Realtime Decoding
• CUDA-Q Solvers by Example
  • Molecular-Hamiltonians
  • ADAPT-VQE
  • VQE
  • QAOA
  • GQE

Performance Studies

• Performance Studies

API Reference

• CUDA-QX Namespaces and Core Library C++ API
• CUDA-Q QEC C++ API
• CUDA-Q QEC Python API
• CUDA-Q Solvers C++ API
• CUDA-Q Solvers Python API

Key Features

CUDA-QX is composed of two distinct libraries that build upon the CUDA-Q programming model. The libraries provided are cudaq-qec, a library enabling performant research workflows for quantum error correction, and cudaq-solvers, a library that provides high-level APIs for common quantum-classical solver workflows.

• 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

• cudaq-solvers (deprecated, superseded by CUDA-Q Algorithms): Performant Quantum-Classical Simulation Workflows

  • Variational Quantum Eigensolver (VQE)

  • ADAPT-VQE implementation that scales via CUDA-Q MQPU.

  • Quantum Approximate Optimization Algorithm (QAOA)

  • Version 0.6.0 is the final planned release; continue with cudaq-algorithms and its documentation.

Indices

• Index

• Search Page