Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunately, current testing methods for QNNs remain underdeveloped, with limited practical utility and insufficient empirical evaluation. As an initial effort, we design a set of superposition-targeted coverage criteria to evaluate QNN state exploration embedded in test suites. To characterize the effectiveness, scalability, and robustness of the criteria, we conduct a comprehensive empirical study using benchmark datasets and QNN architectures. We first evaluate their sensitivity to input diversity under multiple data settings, and analyze their correlation with the number of injected faults. We then assess their scalability to increasing circuit scales. The robustness is further studied under practical quantum constraints including insufficient measurement and quantum noise. The results demonstrate the effectiveness of quantifying test adequacy and the potential applicability to larger-scale circuits and realistic quantum execution, while also revealing some limitations. Finally, we provide insights and recommendations for future QNN testing.

翻译：量子神经网络通过融合量子计算与神经网络技术，已在多种任务中取得初步成功。然而，对其可靠性与鲁棒性的日益关注凸显了系统化测试的必要性。遗憾的是，当前针对量子神经网络的测试方法仍不成熟，存在实用性有限且实证评估不足的问题。作为初步探索，我们设计了一套叠加态导向的覆盖准则，用于评估测试套件中嵌入的量子神经网络状态探索能力。为系统评估这些准则的有效性、可扩展性与鲁棒性，我们采用基准数据集与量子神经网络架构开展了全面的实证研究。首先，我们在多种数据设置下评估了准则对输入多样性的敏感度，并分析了其与注入故障数量的相关性。随后，我们检验了准则对电路规模扩增的可扩展性。此外，在测量不足与量子噪声等实际量子约束条件下，我们进一步研究了准则的鲁棒性。实验结果表明，该准则能有效量化测试充分性，并具备向更大规模电路及实际量子环境扩展应用的潜力，同时也揭示了若干局限性。最后，我们为未来量子神经网络测试提供了研究启示与建议。