Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing interest in quantum machine learning (QML), ensuring the correctness and robustness of software platforms to develop such QML programs is critical. A necessary step for ensuring the reliability of such platforms is to understand the bugs they typically suffer from. To address this need, this paper presents the first comprehensive study of bugs in QML frameworks. We inspect 391 real-world bugs collected from 22 open-source repositories of nine popular QML frameworks. We find that 1) 28% of the bugs are quantum-specific, such as erroneous unitary matrix implementation, calling for dedicated approaches to find and prevent them; 2) We manually distilled a taxonomy of five symptoms and nine root cause of bugs in QML platforms; 3) We summarized four critical challenges for QML framework developers. The study results provide researchers with insights into how to ensure QML framework quality and present several actionable suggestions for QML framework developers to improve their code quality.

翻译：量子计算已崛起为机器学习领域中一个极具前景的方向，相较于经典计算具有显著的算力优势。随着量子机器学习（QML）研究兴趣的日益增长，确保此类QML程序开发软件平台正确性与鲁棒性至关重要。保障这类平台可靠性的必要前提，是理解其常见的缺陷类型。为满足这一需求，本文首次对QML框架中的缺陷展开全面研究。我们系统审查了从九大流行QML框架的22个开源仓库中收集的391个真实缺陷。研究发现：1）28%的缺陷属于量子特有类型（例如错误的酉矩阵实现），亟需专门方法进行检测与预防；2）我们通过人工分析归纳出QML平台缺陷的五类症状与九种根本原因；3）总结出QML框架开发者面临的四大关键挑战。研究结果为如何保障QML框架质量提供了深刻见解，并为QML框架开发者提升代码质量提出了多项可操作建议。