  Quantum computing holds tremendous potential for various applications, but
its security remains a crucial concern. Quantum circuits need high-quality
compilers to optimize the depth and gate count to boost the success probability
on current noisy quantum computers. There is a rise of efficient but
unreliable/untrusted compilers; however, they present a risk of tampering such
as Trojan insertion. We propose TrojanNet, a novel approach to enhance the
security of quantum circuits by detecting and classifying Trojan-inserted
circuits. In particular, we focus on the Quantum Approximate Optimization
Algorithm (QAOA) circuit that is popular in solving a wide range of
optimization problems. We investigate the impact of Trojan insertion on QAOA
circuits and develop a Convolutional Neural Network (CNN) model, referred to as
TrojanNet, to identify their presence accurately. Using the Qiskit framework,
we generate 12 diverse datasets by introducing variations in Trojan gate types,
the number of gates, insertion locations, and compiler backends. These datasets
consist of both original Trojan-free QAOA circuits and their corresponding
Trojan-inserted counterparts. The generated datasets are then utilized for
training and evaluating the TrojanNet model. Experimental results showcase an
average accuracy of 98.80% and an average F1-score of 98.53% in effectively
detecting and classifying Trojan-inserted QAOA circuits. Finally, we conduct a
performance comparison between TrojanNet and existing machine learning-based
Trojan detection methods specifically designed for conventional netlists.
