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：利用量子力学独特性质，量子机器学习有望在传统系统触及性能瓶颈时实现计算突破并拓展认知维度。然而与传统机器学习类似，量子机器学习亦面临对抗性攻击的威胁。量子对抗性机器学习已成为揭示量子机器学习模型在对抗性特征向量攻击时脆弱性的关键工具。本研究深入探索该领域，重点阐明了退极化噪声与对抗鲁棒性之间的相互作用机制。尽管先前研究表明退极化噪声可增强模型对抗威胁的鲁棒性，我们的实验结果却呈现不同图景：有趣的是，在多分类场景中，添加退极化噪声反而中断了鲁棒性提升效应。为验证这一发现，我们在基于量子门的模拟器上对多分类器进行对抗性训练实验，进一步阐释了这一反常现象的内在机理。