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：量子计算已成为机器学习领域的一个有前景方向，相较于经典计算具备显著的计算优势。随着量子机器学习（QML）领域关注度的日益提升，确保开发此类QML程序的软件平台正确性与鲁棒性至关重要。保证此类平台可靠性的必要步骤，是理解其典型错误特征。为满足这一需求，本文首次对QML框架中的错误开展系统性研究。我们共检验了从9个主流QML框架的22个开源仓库收集的391个真实错误。研究发现：1）28%的错误具有量子领域特异性（如不正确的酉矩阵实现），需要专门方法进行检测与预防；2）我们人工归纳出QML平台错误的五大症状与九大根本原因分类体系；3）总结出QML框架开发者面临的四大关键挑战。本研究结果为保障QML框架质量提供了研究启示，并为QML框架开发者改进代码质量提供了若干可操作建议。