Q-Poison: Quantum Adversarial Attacks against QML-driven Interference Classification in O-RAN

The integration of quantum machine learning (QML) within open radio access network (O-RAN) architectures presents a promising frontier for enhancing interference classification and optimizing various 6G network use cases. We introduce a novel hybrid quantum-classical neural network (HQCNN) design that addresses critical challenges in noisy intermediate-scale quantum (NISQ) environments through an innovative feature preprocessing pipeline. By integrating deep convolutional feature extraction via ResNet-18 with principal component analysis (PCA)-based dimensionality reduction, we develop a robust computational framework that mitigates quantum gate errors and enhances computational stability. However, despite these advancements, HQCNNs remain vulnerable to adversarial attacks. Traditional classical backdoor methods are ineffective in the quantum setting due to NISQ noise and gate synthesis complexities, while existing quantum attack techniques suffer from a lack of selectivity and adaptability, making them easy to detect. To address this, we introduce Q-Poison, a novel stealthy attack that leverages parameterized quantum gates to significantly enhance attack success rates. This attack induces systematic misclassification of trigger inputs while maintaining 54.5% clean data accuracy, compared to 78.5% without attack. Our results demonstrate a substantial 1.44-fold reduction in accuracy, highlighting the need for robust defense mechanisms in quantum-enabled networks.