  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\%.
