Abstract: The vulnerability of quantum classifiers under adversarial attacks is one of the fundamental problems in quantum machine learning. The vulnerability of quantum classifiers refers to the property that a quantum classifier may be failed by small perturbations when the quantum system scales up. Such perturbations are also known as quantum adversarial attacks. How to generate small perturbations to fail a quantum classifier is still an open problem. To address this problem, we present a new quantum adversarial attack generation method, the quantum confounding algorithm, which generates perturbations that fail the trained quantum classifier by utilizing the gradient information of the quantum classifier with respect to the input data. Numerical results demonstrate that, compared with the existing quantum adversarial attack generation methods, our quantum confounding algorithm can generate significantly smaller perturbations that lead the quantum classifier to malfunction. This provides a new perspective in understanding the effectiveness and the vulnerability of quantum classifiers.