🤖 AI Summary
This study addresses the complex and poorly understood defects arising from interactions between classical and quantum components in quantum software, where existing research lacks a unified, cross-ecosystem classification methodology. The work proposes the first rule-driven, interpretable, and automatable classification framework, systematically annotating and comparing 17,523 issues across 12 repositories—including Qiskit, Cirq, and PyQuil—along dimensions of defect type, severity, quality attributes, and quantum-specific subcategories. Through statistical validation, machine learning baselines, and longitudinal trend analysis (2017–2025), the study reveals that classical defects constitute 67% of all issues, while quantum-specific defects remain stable at 27–30%, with significant ecosystem-level variations (e.g., prominent compatibility issues in Qiskit). The proposed framework substantially outperforms data-driven models in fine-grained quantum defect identification.
📝 Abstract
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.