2023/06/10 by Pengzhan Zhao, Zhao, Pengzhan, Xiongfei Wu +7 · 2 citations
Computer Science · Engineering · #Advancements in Semiconductor Devices and Circuit Design #Cloud Computing and Resource Management #FOS: Computer and information sciences #Quantum Computing Algorithms and Architecture #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2306.06369
openalex publication_date 2023/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.