  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.
