Machine learning has been extensively applied for classical software testing activities such as test generation, minimization, and prioritization. Along the same lines, there has been interest in applying quantum machine learning to classical software testing. For example, Quantum Extreme Learning Machines (QELMs) were recently applied for testing classical software of industrial elevators. However, studies on QELMs, whether in software testing or other areas, used ideal simulators that fail to account for the noise in current quantum computers. While ideal simulations offer insight into QELM's theoretical capabilities, they do not enable studying their performance on current noisy quantum computers. To this end, we study how quantum noise affects QELM in three industrial classical software testing case studies, providing insights into QELMs' robustness to noise for software testing applications. Such insights assess QELMs potential as a viable solution for software testing problems in today's noisy quantum computing. Our results show that QELMs are significantly affected by quantum noise, with a performance drop of 250% in regression and 50% in classification software testing tasks. Quantum noise also increases uncertainty in QELM models, producing a saturation effect where larger qubit counts make the models increasingly random and unreliable. While error mitigation techniques can enhance noise resilience, achieving an average 3% performance drop in classification, their effectiveness varies by context. For classification tasks, QLEAR performs well, whereas Zero Noise Extrapolation is more effective for regression and smaller qubit counts. However, no single mitigation approach consistently reduces uncertainty across tasks or scales reliably as the number of qubits increases, highlighting the need for QELM-tailored strategies.

翻译：机器学习已广泛应用于经典软件测试活动，如测试生成、最小化和优先级排序。在此背景下，将量子机器学习应用于经典软件测试也引起了研究兴趣。例如，量子极限学习机（QELMs）最近被应用于工业电梯经典软件的测试。然而，无论是软件测试还是其他领域，现有关于QELMs的研究均采用理想模拟器，未能考虑当前量子计算机中的噪声影响。虽然理想模拟有助于理解QELM的理论能力，但无法评估其在当前含噪声量子计算机上的实际表现。为此，我们通过三个工业级经典软件测试案例研究，探究量子噪声如何影响QELM的性能，从而揭示QELM在软件测试应用中对噪声的鲁棒性。这些发现评估了QELM在当前含噪声量子计算环境下作为软件测试问题可行解决方案的潜力。研究结果表明，量子噪声对QELMs产生显著影响：在回归测试任务中性能下降达250%，在分类测试任务中下降50%。量子噪声还增加了QELM模型的不确定性，产生饱和效应——当量子比特数增加时，模型会逐渐变得随机且不可靠。虽然误差缓解技术能提升噪声鲁棒性（在分类任务中平均性能下降降至3%），但其效果因具体情境而异。对于分类任务，QLEAR表现良好；而在回归任务和较少量子比特场景中，零噪声外推法更为有效。然而，没有任何一种缓解方法能在所有任务中持续降低不确定性，或随量子比特数增加而保持可靠扩展，这凸显了开发针对QELM定制化策略的必要性。