为量子神经网络设计针对性测试标准，评估其状态探索效果。
Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks
- 提出针对叠加态的测试覆盖准则，用于衡量量子神经网络的状态探索
- 在多种数据设置下验证准则对输入多样性的敏感性及故障注入关联性
- 适用于真实量子环境，适合关注量子模型可靠性的研究者参考
量子神经网络（QNN）通过结合量子计算与神经网络在多个任务中取得初步成功。然而，其可靠性和鲁棒性日益引发关注，亟需系统性测试方法。目前QNN测试手段仍不成熟，实用性和实证评估不足。为此，我们设计了一套针对叠加态的覆盖准则，用于评估测试用例对QNN状态探索的覆盖程度。通过基准数据集和QNN架构开展全面实证研究，考察准则在不同数据设置下的输入多样性敏感性，分析其与注入故障数的相关性；进一步评估其随电路规模增长的可扩展性，并在测量不足和量子噪声等实际量子约束下检验其鲁棒性。结果表明，该准则能有效量化测试充分性，具备向大规模电路和真实量子执行场景拓展的潜力，同时也揭示了部分局限性。最后，我们为未来QNN测试提供洞见与建议。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。