首次在超导量子处理器上测试20比特量子神经网络的抗攻击能力
Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor
- 设计高效量子对抗攻击算法，量化评估量子神经网络鲁棒性
- 实验显示量子神经网络比经典模型更抗攻击，因量子噪声带来优势
- 攻击效果逼近理论极限，验证了保真度鲁棒性边界的紧致性
量子机器学习（QML）模型与经典模型一样，易受对抗攻击影响，阻碍其安全部署。本文首次在超导处理器上对20比特量子神经网络（QNN）分类器进行了系统性实验鲁棒性基准测试。所提出的基准框架包含专为QNN设计的高效对抗攻击算法，可实现对抗鲁棒性的定量表征及鲁棒性边界分析。分析表明，对抗训练通过正则化输入梯度，显著降低对目标扰动的敏感性，大幅提升QNN鲁棒性；此外，量子神经网络相比经典神经网络展现出更强的对抗鲁棒性，这一优势归因于固有的量子噪声。进一步地，从攻击实验中提取的经验上界与理论下界仅相差3×10⁻³，强烈证实了攻击的有效性以及基于保真度的鲁棒性边界之紧致性。该工作建立了评估和提升量子对抗鲁棒性的关键实验框架，为安全可靠的量子机器学习应用铺平道路。
原文摘要 · Abstract (English)
Quantum machine learning (QML) models, like their classical counterparts, are vulnerable to adversarial attacks, hindering their secure deployment. Here, we report the first systematic experimental robustness benchmark for 20-qubit quantum neural network (QNN) classifiers executed on a superconducting processor. Our benchmarking framework features an efficient adversarial attack algorithm designed for QNNs, enabling quantitative characterization of adversarial robustness and robustness bounds. From our analysis, we verify that adversarial training reduces sensitivity to targeted perturbations by regularizing input gradients, significantly enhancing QNN's robustness. Additionally, our analysis reveals that QNNs exhibit superior adversarial robustness compared to classical neural networks, an advantage attributed to inherent quantum noise. Furthermore, the empirical upper bound extracted from our attack experiments shows a minimal deviation ($3 \times 10^{-3}$) from the theoretical lower bound, providing strong experimental confirmation of the attack's effectiveness and the tightness of fidelity-based robustness bounds. This work establishes a critical experimental framework for assessing and improving quantum adversarial robustness, paving the way for secure and reliable QML applications.
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