一句话：提出多样性引导的量子程序测试框架 NovaQ，结合电路参数变异与新颖性评估，生成多样量子态输入以发现更多缺陷。
量子程序依托量子电路求解经典机器难以处理的问题，随着量子计算的发展，其可靠性保障日益重要。本文提出 NovaQ——一种多样性引导的量子程序测试框架，由基于分布的测试用例生成器与新颖性驱动的评估模块组成。生成器通过变异电路参数产生多样的量子态输入；评估器基于内部电路状态指标（幅度、相位与纠缠）量化行为新颖性。通过挑选映射到度量空间中覆盖稀疏区域的输入，NovaQ 能有效探索测试不足的程序行为。作者在不同规模与复杂度的量子程序上对其进行评估，实验结果表明 NovaQ 在测试输入多样性上持续优于现有基线方法，并能检测出更多缺陷。
Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduces NovaQ, a diversity-guided testing framework for quantum programs. NovaQ combines a distribution-based test case generator with a novelty-driven evaluation module. The generator produces diverse quantum state inputs by mutating circuit parameters, while the evaluator quantifies behavioral novelty based on internal circuit state metrics, including magnitude, phase, and entanglement. By selecting inputs that map to infrequently covered regions in the metric space, NovaQ effectively explores under-tested program behaviors. We evaluate NovaQ on quantum programs of varying sizes and complexities. Experimental results show that NovaQ consistently achieves higher test input diversity and detects more bugs than existing baseline approaches.
DOI 原文 ·
@inproceedings{paperbot9338,
title = {NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation},
author = {Tiancheng Jin and Shangzhou Xia and Jianjun Zhao},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00335}
}