  Quantum Machine Learning (QML) has emerged as a promising field of research,
aiming to leverage the capabilities of quantum computing to enhance existing
machine learning methodologies. Recent studies have revealed that, like their
classical counterparts, QML models based on Parametrized Quantum Circuits
(PQCs) are also vulnerable to adversarial attacks. Moreover, the existence of
Universal Adversarial Perturbations (UAPs) in the quantum domain has been
demonstrated theoretically in the context of quantum classifiers. In this work,
we introduce QuGAP: a novel framework for generating UAPs for quantum
classifiers. We conceptualize the notion of additive UAPs for PQC-based
classifiers and theoretically demonstrate their existence. We then utilize
generative models (QuGAP-A) to craft additive UAPs and experimentally show that
quantum classifiers are susceptible to such attacks. Moreover, we formulate a
new method for generating unitary UAPs (QuGAP-U) using quantum generative
models and a novel loss function based on fidelity constraints. We evaluate the
performance of the proposed framework and show that our method achieves
state-of-the-art misclassification rates, while maintaining high fidelity
between legitimate and adversarial samples.
