评估面向叠加的覆盖标准用于量子神经网络
Assessing Superposition-Targeted Coverage Criteria for Quantum Neural Networks
AI总结：
本文提出面向叠加的覆盖标准用于评估QNN状态探索，通过实证研究验证其有效性及可扩展性，揭示了测试方法的潜力与局限性。
摘要：
量子神经网络（QNNs）通过整合量子计算和神经网络在各种任务中取得了初步成功。然而，对其可靠性和鲁棒性的日益关注凸显了系统测试的必要性。不幸的是，当前QNN的测试方法仍然不完善，缺乏实际应用价值和充分的实证评估。作为初步尝试，我们设计了一组面向叠加的覆盖标准来评估嵌入在测试套件中的QNN状态探索。为了表征这些标准的有效性、可扩展性和鲁棒性，我们使用基准数据集和QNN架构进行了全面的实证研究。我们首先在多种数据设置下评估其对输入多样性的敏感性，并分析其与注入故障数量的相关性。然后评估其可扩展性到增加的电路规模。鲁棒性进一步在实际量子约束下研究，包括测量不足和量子噪声。结果展示了量化测试充分性有效性的潜力，并适用于更大规模的电路和现实量子执行，同时也揭示了一些局限性。最后，我们为未来的QNN测试提供了见解和建议。
英文摘要：
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.
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