Quantum software testing is important for reliable quantum software engineering. Despite recent advances, existing quantum software testing approaches rely on simple test inputs and statistical oracles, costly program specifications, and limited validation on real quantum computers. To address these challenges, we propose SB-QOPS, a search-based quantum program testing approach via commuting Pauli strings. SB-QOPS, as a direct extension to a previously proposed QOPS approach, redefines test cases in terms of Pauli strings and introduces a measurement-centric oracle that exploits their commutation properties, enabling effective testing of quantum programs while reducing the need for full program specifications. By systematically exploring the search space through an expectation-value-based fitness function, SB-QOPS improves test budget utilization and increases the likelihood of uncovering subtle faults. We conduct a large-scale empirical evaluation on quantum circuits of up to 29 qubits on real quantum computers and emulators. We assess three search strategies: Genetic Algorithm, Hill Climbing, and the (1+1) Evolutionary Algorithm, and evaluate SB-QOPS under both simulated and real noisy conditions. Experiments span three quantum computing platforms: IBM, IQM, and Quantinuum. Results show that SB-QOPS significantly outperforms QOPS, achieving a fault-detection score of 100% for circuits up to 29 qubits, and demonstrating portability across quantum platforms.

翻译：量子软件测试对于可靠的量子软件工程至关重要。尽管近期取得进展，现有量子软件测试方法仍依赖于简单测试输入与统计预言机、昂贵的程序规约，以及在真实量子计算机上的有限验证。为应对这些挑战，我们提出SB-QOPS——一种基于对易泡利字符串的搜索式量子程序测试方法。作为对先前提出的QOPS方法的直接扩展，SB-QOPS以泡利字符串重新定义测试用例，并引入一种利用其对易特性的测量中心化预言机，从而在减少对完整程序规约依赖的同时实现对量子程序的有效测试。通过基于期望值的适应度函数系统探索搜索空间，SB-QOPS提升了测试预算的利用效率，并增加了发现细微缺陷的可能性。我们在真实量子计算机和模拟器上对高达29量子位的量子电路进行了大规模实证评估，考察了遗传算法、爬山算法和(1+1)进化算法三种搜索策略，并在模拟与真实噪声环境下评估了SB-QOPS的性能。实验覆盖IBM、IQM和Quantinuum三大量子计算平台。结果表明，SB-QOPS显著优于QOPS方法，对高达29量子位的电路实现了100%的缺陷检测率，并展现出跨量子计算平台的可移植性。