Quantum Machine Learning for 6G Network Intelligence and Adversarial Threats

Nguyen, Van-Linh; Nguyen, Lan-Huong; Hwang, Ren-Hung; Canberk, Berk; Duong, Trung Q

Abstract

Quantum computing has been a major priority for several nations and prominent institutions in their pursuit of a transformative breakthrough in the fields of computation and encryption. By using the principles of quantum mechanics, particularly quantum superposition and entanglement, quantum computing and quantum machine learning (QML) have the potential to enhance artificial intelligence (AI) and achieve quantum supremacy with unprecedented computational power. However, despite its exceptional learning capabilities, QML-based applications face several emerging security threats. Unlike previous studies focused on classical quantum cryptography and secure quantum communications, this work investigates adversarial risks in QML-assisted network functions and digital twin applications. Specifically, we highlight vulnerabilities such as quantum kernel poisoning, backdoor attacks, and adversarial noise. Key findings reveal that adversaries can intercept quantum states in transit, manipulate parameterized quantum circuits (PQCs), and exploit variational quantum algorithms (VQAs) through adversarial qubit perturbations. These attacks can mislead QML-based optimization processes, leading to incorrect digital twin predictions, faulty resource allocation, or disruptions in QML-aided network functions. To mitigate these risks, defense strategies such as quantum-safe cryptography, data sanitization, adversarial training, defensive distillation, and gradient masking in quantum circuit design can be employed. However, the key issue is the absence of robust security solutions for real-world deployment. Future research should examine the trade-off between adversarial robustness and generative learning performance. Key areas include quantum state discrimination, secure quantum federated learning, quantum decoherence control, and secure quantum semantic communications for real-world deployment. Index Terms-Quantum machine learning, quantum circuits, quantum kernel poisoning, quantum adversarial attacks, adver-sarial defense, 6G quantum networks, semantic communications

Citation

Nguyen, V.-L., Nguyen, L.-H., Hwang, R.-H., Canberk, B., & Duong, T. Q. (in press). Quantum Machine Learning for 6G Network Intelligence and Adversarial Threats. IEEE Communications Standards Magazine,

• Journal Article Type: Article
• Acceptance Date: Mar 26, 2025
• Deposit Date: Apr 1, 2025
• Journal: IEEE Communications Standards Magazine
• Print ISSN: 2471-2825
• Electronic ISSN: 2471-2833
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Keywords: Quantum machine learning, quantum circuits, quantum kernel poisoning, quantum adversarial attacks, adversarial defense, 6G quantum networks, semantic communications
• Public URL: http://researchrepository.napier.ac.uk/Output/4208644