Analyzing the Evolution and Maintenance of Quantum Software Repositories
Krishna Upadhyay, Vinaik Chhetri, A. B. Siddique, Umar Farooq
TL;DR
The paper presents a large-scale mining study of quantum software repositories, analyzing 24,122 GitHub projects with over 1.2 million commits from 10,697 developers to understand the field's evolution and maintenance practices. Using dataset-driven methods, including language/framework mapping and DistilBERT/BERTopic-based topic analyses, it reveals rapid growth since 2017, with Python and Qiskit leading the ecosystem and emerging tools gaining traction. It shows that most commits are perfective, a substantial portion of issues are quantum-specific, and maintenance demands stronger tooling, automated testing, and better issue-tracking to improve reliability. The work provides actionable insights for tooling, documentation, and community governance, and openly shares its dataset to spur further research and tool development in quantum software engineering.
Abstract
Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a large-scale mining analysis of over 21,000 GitHub repositories, containing 1.2 million commits from more than 10,000 developers, to examine the evolution and maintenance of quantum software. We analyze repository growth, programming language and framework adoption, and contributor trends, revealing a 200% increase in repositories and a 150% rise in contributors since 2017. Additionally, we investigate software development and maintenance practices, showing that perfective commits dominate (51.76%), while the low occurrence of corrective commits (18.54%) indicates potential gaps in bug resolution. Furthermore, 34% of reported issues are quantum-specific, highlighting the need for specialized debugging tools beyond conventional software engineering approaches. This study provides empirical insights into the software engineering challenges of quantum computing, offering recommendations to improve development workflows, tooling, and documentation. We are also open-sourcing our dataset to support further analysis by the community and to guide future research and tool development for quantum computing. The dataset is available at: https://github.com/kriss-u/QRepoAnalysis-Paper