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.

翻译：利用量子力学的独特特性，量子机器学习（QML）有望在传统系统达到极限时带来计算突破和新的视角。然而，与经典机器学习类似，QML也并非免疫于对抗性攻击。量子对抗机器学习在揭示QML模型面对精心设计的对抗特征向量时的弱点方面发挥了关键作用。深入这一领域，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。虽然先前的研究通过去极化噪声增强了对抗威胁下的鲁棒性，但我们的发现呈现出不同的图景。有趣的是，去极化噪声的加入中断了在多类分类场景中提供进一步鲁棒性的效果。为了巩固我们的发现，我们使用基于门的量子模拟器对多类分类器进行了对抗性训练实验，进一步阐明了这一意外行为。