  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.
