NVIDIA/cuda-quantum

C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows

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Summary Information

Updated 31 minutes ago
Added to GitGenius on March 11th, 2026
Created on March 14th, 2023
Open Issues & Pull Requests: 526 (-1)
Number of forks: 445
Total Stargazers: 1,118 (+0)
Total Subscribers: 23 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 4.2 days
Mean response time: 97.4 days
90th percentile: 343.6 days
Tracked items: 857

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 31% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. Work labelled "python-lang" is answered fastest, typically in about 3 days, while "maintenance" waits about 5 months. 50% of tracked open issues have had no activity in three months. Only 6% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 374
New in 7 days: 25
Closed in 7 days: 12
Avg open age: 467 days
Stale 30+ days: 343
Stale 90+ days: 313

Recent activity

Opened in 7 days: 24
Closed in 7 days: 10
Comments in 7 days: 9
Events in 7 days: 37

Top labels

  • stale-notified (403)
  • enhancement (225)
  • bug (111)
  • python-lang (63)
  • Exalate (50)
  • documentation (40)
  • build (36)
  • good first issue (36)

Detailed Description

CUDA-Q is NVIDIA's open-source platform for hybrid quantum-classical computing, providing C++ and Python support for programming systems that integrate quantum processing units, GPUs, and CPUs. The repository contains the complete source code for the CUDA-Q toolkit, including the nvq++ compiler, the CUDA-Q runtime, and integrated CPU and GPU backends designed to accelerate application development and testing. The platform enables developers to write unified programs that orchestrate quantum and classical computing resources within a single heterogeneous system.

The toolkit is classified across multiple quantum computing domains including quantum programming, quantum simulation, quantum machine learning, and QPU interoperability. It also emphasizes high-performance parallel computing and GPU acceleration, reflecting NVIDIA's focus on leveraging graphics processors alongside quantum hardware. The repository is written primarily in C++ with Python bindings, making it accessible to both systems-level developers and application scientists.

The project maintains active development with substantial community engagement.

This cross-project involvement indicates that CUDA-Q is positioned within a broader NVIDIA software stack for AI and scientific computing.

Documentation is comprehensive, with official installation instructions available through the CUDA-Q documentation site and development packages deployed on the GitHub Container Registry. The repository provides building instructions for developers who want to compile from source or contribute modifications.

The platform explicitly welcomes contributions through multiple channels, including the Discussions tab for feature ideas and feedback, issue tracking for bug reports, and pull requests for code contributions. Security concerns and code of conduct violations can be reported directly to [email protected]. This structured approach to community engagement, combined with the rapid response times and active triage, demonstrates NVIDIA's commitment to maintaining CUDA-Q as a collaborative, community-driven quantum computing platform.