AdvQuNN：一种分析量子卷积神经网络对抗鲁棒性的方法
AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks
AI总结：
本文提出AdvQuNN方法，通过不同参数化量子电路和对抗攻击评估量子卷积神经网络的鲁棒性，发现其相比经典网络在MNIST和FMNIST上鲁棒性分别提升60%和40%。
摘要：
量子计算的最新进展推动了混合量子神经网络（HQNNs）的发展，该网络采用量子层和经典层的混合集合，例如量子卷积神经网络（QuNNs）。尽管已有多项研究展示了经典神经网络的安全威胁（如对抗攻击），但它们对QuNNs的影响仍相对未被探索。本研究通过设计AdvQuNN来解决这一问题，这是一种专门的方法论，用于调查HQNNs（如QuNNs）对对抗攻击的鲁棒性。它采用不同类型的Ansatz作为参数化量子电路以及不同类型的对抗攻击。本研究旨在严格评估量子电路架构对QuNN模型弹性的影响，这为增强QuNNs的鲁棒性和推进量子网络安全领域开辟了新途径。我们的结果表明，与经典卷积网络相比，QuNNs在MNIST数据集上的鲁棒性提高了60%，在FMNIST数据集上提高了40%。
英文摘要：
Recent advancements in quantum computing have led to the development of hybrid quantum neural networks (HQNNs) that employ a mixed set of quantum layers and classical layers, such as Quanvolutional Neural Networks (QuNNs). While several works have shown security threats of classical neural networks, such as adversarial attacks, their impact on QuNNs is still relatively unexplored. This work tackles this problem by designing AdvQuNN, a specialized methodology to investigate the robustness of HQNNs like QuNNs against adversarial attacks. It employs different types of Ansatzes as parametrized quantum circuits and different types of adversarial attacks. This study aims to rigorously assess the influence of quantum circuit architecture on the resilience of QuNN models, which opens up new pathways for enhancing the robustness of QuNNs and advancing the field of quantum cybersecurity. Our results show that, compared to classical convolutional networks, QuNNs achieve up to 60\% higher robustness for the MNIST and 40\% for FMNIST datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
