TrojanNet: Detecting Trojans in Quantum Circuits using Machine Learning

TL;DR

TrojanNet is a machine learning-based method that accurately detects Trojan-inserted quantum circuits, specifically QAOA circuits, achieving over 98% accuracy, thereby enhancing quantum circuit security against malicious tampering.

Contribution

This paper introduces TrojanNet, a CNN-based approach tailored for quantum circuits, marking a novel application of ML for Trojan detection in quantum computing.

Findings

Achieved 98.80% accuracy in Trojan detection

Created diverse datasets with various Trojan insertions

Outperformed existing Trojan detection methods for netlists

Abstract

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.