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.

通过构建一类本质上为ε-差分隐私的噪声通道，我们成功地复制了退极化和随机旋转通道的ε-差分隐私边界，从而验证了我们框架的广阔普遍性。我们还使用半定规划构建了一个最优鲁棒通道，在小规模实验评估中证明了使用最优噪声通道相对于退极化噪声的好处，特别是在提高对抗精度方面。此外，我们评估了变量α和γ对可认证的鲁棒性的影响，并研究了不同编码方法对分类器鲁棒性的影响。

构建增强量子机器学习鲁棒性的最佳噪声通道