Analyzing the Evolution and Maintenance of Quantum Computing Repositories

2025-01-12

AI Summary

Quantum software maintenance faces critical bottlenecks—including insufficient corrective commits and a lack of quantum-specific tooling—hindering ecosystem maturity. This study conducts the first large-scale empirical analysis of over 21,000 open-source quantum computing repositories on GitHub, encompassing more than 1.2 million commits and 10,000+ developers. Methodologically, it integrates software archaeology, metadata mining, commit-pattern analysis, issue classification modeling, and community network analysis. Key findings reveal: (1) an overabundance of perfective updates and a severe shortage of corrective commits; (2) ~33% of issues require quantum-specific debugging and verification tools; and (3) repository and contributor counts have grown by 200% and 150%, respectively, since 2017. Contributions include actionable, ecosystem-targeted improvement recommendations for quantum programming practice and the public release of the first large-scale quantum software development dataset.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quantum computing is an emerging field with significant potential, yet software development and maintenance challenges limit its accessibility and maturity. This work investigates the current state, evolution, and maintenance practices in the quantum computing community by conducting a large-scale mining analysis of over 21,000 quantum software repositories on GitHub, containing more than 1.2 million commits contributed by over 10,000 unique developers. Specifically, the focus of this paper is to: (i) assess the community's status and growth by examining the popularity of quantum computing, trends in programming languages and framework usage, growth of contributors, and insights from repository documentation; and (ii) analyze maintenance practices through commit patterns, issue classification, and maintenance levels. Our findings indicate rapid growth in the quantum computing community, with a 200% increase in the number of repositories and a 150% rise in contributors since 2017. Our analysis of commits shows a strong focus on perfective updates, while the relatively low number of corrective commits highlights potential gaps in bug resolution. Furthermore, one-third of the quantum computing issues highlight the need for specialized tools in addition to general software infrastructure. In summary, this work provides a foundation for targeted improvements in quantum software to support sustained growth and technical advancement. Based on our analysis of development activity, community structure, and maintenance practices, this study offers actionable recommendations to enhance quantum programming tools, documentation, and resources. 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.

Problem

• Quantum Computing
• Software Development
• Challenges

Innovation

• Quantum Computing Software Analysis
• Software Maintenance Imbalance
• Open Research Data for Tool Development