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ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Towards Quantum Program Bug Datasets and Benchmarking

Tingting Li, Jiongchi Yu, Zhikang Fan, Junqing Wang, Yao Zhai, Shenglong Zhang, Yu Peng, Yuhan Chen, Junyu Chen, Fanqi Kong, Zhaoxuan Li, Xiaofei Yue, Ziming Zhao, Jianwei Yin

Published Aug 7, 2026• 0 citations
Publisher
DOI
10.1145/3770855.3817560

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Automated Program Repair (APR) is a vital area in software engineering that generates automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.
Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. However, there is no suitable benchmark suite that can systematically evaluate the debugging and testing methods of quantum programs until now. This paper proposes Bugs4Q, a benchmark of thirty-six real, manually validated Qiskit bugs from four popular Qiskit elements (Terra, Aer, Ignis, and Aqua), supplemented with the test cases for reproducing buggy behaviors. Bugs4Q also provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding test cases, facilitating the reproducible empirical studies and comparisons of Qiskit program debugging and testing tools. Bugs4Q is publicly available at https://github.com/Z-928/Bugs4Q