Abstract
With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident. In this work, we outline the connection between quantum noise channels and differential privacy (DP), by constructing a family of noise channels which are inherently $\epsilon$-DP: $(\alpha, \gamma)$-channels. Through this approach, we successfully replicate the $\epsilon$-DP bounds observed for depolarizing and random rotation channels, thereby affirming the broad generality of our framework. Additionally, we use a semi-definite program to construct an optimally robust channel. In a small-scale experimental evaluation, we demonstrate the benefits of using our optimal noise channel over depolarizing noise, particularly in enhancing adversarial accuracy. Moreover, we assess how the variables $\alpha$ and $\gamma$ affect the certifiable robustness and investigate how different encoding methods impact the classifier's robustness.
Abstract (translated)
在量子机器学习（QML）快速发展的背景下，增强对抗攻击的安全措施和保护QML模型的迫切需要变得越来越明显。在这项工作中，我们概述了量子噪声信道和差分隐私（DP）之间的联系，通过构建一个固有$\epsilon$-DP的噪声信道家族：($\alpha, \gamma$)信道。通过这种方法，我们成功复制了用于去偏振和随机旋转信道的$\epsilon$-DP界值，从而证实了我们框架的广泛适用性。此外，我们使用半定规划方法构建了最优鲁棒信道。在小型实验评估中，我们证明了使用我们最优的噪声信道比使用去偏振噪声的优势，尤其是在提高攻击准确性方面。此外，我们还研究了变量$\alpha$和$\gamma$如何影响可证明鲁棒性，以及不同编码方法如何影响分类器的鲁棒性。
URL
https://arxiv.org/abs/2404.16417
