Leveraging the unique properties of quantum mechanics, Quantum Machine
Learning (QML) promises computational breakthroughs and enriched perspectives
where traditional systems reach their boundaries. However, similarly to
classical machine learning, QML is not immune to adversarial attacks. Quantum
adversarial machine learning has become instrumental in highlighting the weak
points of QML models when faced with adversarial crafted feature vectors.
Diving deep into this domain, our exploration shines light on the interplay
between depolarization noise and adversarial robustness. While previous results
enhanced robustness from adversarial threats through depolarization noise, our
findings paint a different picture. Interestingly, adding depolarization noise
discontinued the effect of providing further robustness for a multi-class
classification scenario. Consolidating our findings, we conducted experiments
with a multi-class classifier adversarially trained on gate-based quantum
simulators, further elucidating this unexpected behavior.

利用量子力学的独特性质，量子机器学习（QML）在传统系统到达极限时承诺实现计算突破和丰富视角。然而，类似于经典机器学习，QML 也无法免受敌对攻击。量子敌对机器学习在凸显面对敌对性特征向量时的 QML 模型的弱点方面发挥了重要作用。在深入研究这个领域的同时，我们的探索揭示了去极化噪声与对抗鲁棒性之间的相互作用。虽然以前的结果通过去极化噪声增强了抗敌对威胁的鲁棒性，但我们的发现描绘了不同的情况。有趣的是，在多类别分类场景中添加去极化噪声取消了进一步提供鲁棒性的效果。验证我们的发现，我们进行了实验，对基于门的量子模拟器进行了敌对训练的多类别分类器进一步阐明了这种意外行为。