We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA), since it does not embed any trigger in the inputs of the victim QNN or require the access to original training datasets. Compared to a DPBA, QTrojan improves the clean data accuracy by 21\% and the attack success rate by 19.9\%.
@article{arxiv.2302.08090,
title = {QTrojan: A Circuit Backdoor Against Quantum Neural Networks},
author = {Cheng Chu and Lei Jiang and Martin Swany and Fan Chen},
journal= {arXiv preprint arXiv:2302.08090},
year = {2023}
}