An Empirical Study of Bugs in Quantum Machine Learning Frameworks

by   Pengzhan Zhao, et al.

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 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.


page 1

page 2

page 3

page 4


Bugs in Quantum Computing Platforms: An Empirical Study

The interest in quantum computing is growing, and with it, the importanc...

A Comprehensive Study of Bug Fixes in Quantum Programs

As quantum programming evolves, more and more quantum programming langua...

Bugs in Machine Learning-based Systems: A Faultload Benchmark

The rapid escalation of applying Machine Learning (ML) in various domain...

MorphQ: Metamorphic Testing of Quantum Computing Platforms

As quantum computing is becoming increasingly popular, the underlying qu...

Bug Characteristics in Quantum Software Ecosystem

With the advance in quantum computing in recent years, quantum software ...

The challenge of reproducible ML: an empirical study on the impact of bugs

Reproducibility is a crucial requirement in scientific research. When re...

Characterizing Bugs in Python and R Data Analytics Programs

R and Python are among the most popular languages used in many critical ...

Please sign up or login with your details

Forgot password? Click here to reset