AI 中文总结
本研究对Qiskit等12个量子软件仓库的17523个缺陷开展跨生态系统比较，采用基于规则的分类框架，发现经典缺陷占比67%，所提框架在细粒度量子缺陷检测上优于机器学习基线。
AI 中文摘要
量子软件工程因经典组件与量子组件的交互而面临独特挑战，这种交互会产生复杂且常难以理解的缺陷模式。表征这些缺陷对于推进量子生态系统中的测试、调试和质量保证至关重要。本文对来自Qiskit的12910个问题，以及来自包括Cirq和PyQuil在内的11个额外仓库的4613个问题展开比较研究。我们使用基于规则的分类框架，从缺陷类型、类别、严重程度、质量属性和量子特定子类型等维度对缺陷进行分析。结果显示，在各生态系统中经典缺陷始终占据主导地位（占比67%），而量子特定缺陷占比为27%-30%。不同生态系统呈现出特定趋势：Qiskit仓库存在更多与兼容性相关的缺陷，而其他生态系统的语法缺陷和量子特定缺陷占比更高。在两类生态系统中，量子特定缺陷以门和电路问题为主，不过非Qiskit项目展现出更广泛的多样性，包括算法、资源以及混合接口相关缺陷。统计验证确认该框架在缺陷类型层面具备通用性，同时能检测到更精细层面的显著差异。与4种监督机器学习基线的基准测试进一步表明，该基于规则的框架始终优于数据驱动模型，尤其在细粒度量子特定子类型的检测上表现突出；而2017-2025年的纵向分析显示，量子特定缺陷随时间推移保持相对稳定，并未呈现持续增长态势。本研究首次对量子软件中的缺陷分布开展跨生态系统比较，证明了可解释、可自动化的基于规则框架在指导测试、调试和质量保证方面的实用性。
英文摘要
Quantum software engineering faces unique challenges due to the interaction of classical and quantum components, which produce complex and often poorly understood bug patterns. Characterizing these bugs is essential for advancing testing, debugging, and quality assurance in quantum ecosystems. This paper presents a comparative study of 12,910 issues from Qiskit and 4,613 issues from 11 additional repositories, including Cirq and PyQuil. Using a rule-based classification framework, we analyze bugs by type, category, severity, quality attributes, and quantum-specific subtypes. Results show that classical bugs consistently dominate (67%) across ecosystems, while quantum-specific bugs account for 27-30%. Ecosystem-specific trends emerge: Qiskit repositories exhibit more compatibility related bugs, whereas other ecosystems show higher syntax and quantum-specific bug rates. Across both ecosystems, gate and circuit issues dominate quantum-specific bugs, though non-Qiskit projects reveal broader diversity, including algorithmic, resource, and hybrid-interface issues. Statistical validation confirms that the framework generalizes at the bug-type level while detecting significant variations at finer levels. Benchmarking against four supervised machine-learning baselines further shows that the rule-based framework consistently outperforms data-driven models, particularly for fine-grained quantum-specific subtypes, while longitudinal analysis (2017-2025) indicates that quantum- specific bugs remain relatively stable over time rather than exhibiting a steady increase. This study provides the first cross- ecosystem comparison of bug distributions in quantum software, demonstrating the utility of an interpretable, automation-ready, rule-based framework for guiding testing, debugging, and quality assurance.
Journal refIEEE 2026 9th International Conference on Computing Methodologies and Communication (ICCMC)
DOI：10.1109/ICCMC69250.2026.11624722
