The evolution of 6G-enabled Cellular Vehicle-to-Everything (C-V2X) networks is set to transform intelligent transportation by enabling ultra-reliable, low-latency, and high-throughput connectivity for connected and autonomous vehicles. However, in the light of emerging quantum computing and the growing dependence on distributed edge intelligence and collaborative learning in these networks introduces new security and privacy challenges. This paper proposes a novel quantum-resistant split learning architecture for 6 G C -V2X systems, integrating post-quantum cryptography (PQC) at critical stages of communication and computation. The proposed architecture employs quantum-safe techniques for initial authentication, dynamically divides the deep learning model between vehicles and roadside units, encrypts intermediate data with quantum-safe algorithms, and secures model aggregation using lattice-based homomorphic encryption. Furthermore, a mobility-aware, multitier key management system facilitates smooth and secure handovers, where the hardware acceleration mitigates PQC overhead to maintain real-time performance with the 6 G ultra-reliable low-latency communication (URLLC) constraints, delivering a comprehensive, future-proof framework for privacy-preserving, quantum-secure collaborative learning in next-generation vehicular edge networks.
The evolution of 6G-enabled Cellular Vehicle-to-Everything (C-V2X) networks is set to transform intelligent transportation by enabling ultra-reliable, low-latency, and high-throughput connectivity for connected and autonomous vehicles. However, in the light of emerging quantum computing and the growing dependence on distributed edge intelligence and collaborative learning in these networks introduces new security and privacy challenges. This paper proposes a novel quantum-resistant split learning architecture for 6 G C -V2X systems, integrating post-quantum cryptography (PQC) at critical stages of communication and computation. The proposed architecture employs quantum-safe techniques for initial authentication, dynamically divides the deep learning model between vehicles and roadside units, encrypts intermediate data with quantum-safe algorithms, and secures model aggregation using lattice-based homomorphic encryption. Furthermore, a mobility-aware, multitier key management system facilitates smooth and secure handovers, where the hardware acceleration mitigates PQC overhead to maintain real-time performance with the 6 G ultra-reliable low-latency communication (URLLC) constraints, delivering a comprehensive, future-proof framework for privacy-preserving, quantum-secure collaborative learning in next-generation vehicular edge networks.
With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and model collaboration. Conventional machine learning struggles with high-dimensional state spaces, slow convergence, and poor generalization under heterogeneous V2X nodes, rapidly varying channels, and multimodal sensing data in V2X systems. To address these issues, we propose a quantum-enhanced framework for V2X communication and model aggregation that targets efficient, robust, and intelligent transportation in 6G, which includes four modules: the channel-adaptive semantic communication module, the multimodal fusion module, the model transfer module, and the federated aggregation module. Specifically, the channel-adaptive semantic communication module leverages quantum convolutional neural networks (CNN) and quantum distortion metrics to enable efficient transmission and strong generalization across diverse conditions. The multimodal fusion module exploits quantum attention and entanglement to compress features and associate semantics across heterogeneous data. The model transfer module employs quantum reinforcement learning to model decision-making and improve adaptability in dynamic environments. The federated aggregation module integrates quantum tensor decomposition with backpropagation-based corrections to provide privacy preservation with low overhead and to strengthen global model robustness. This work outlines a new paradigm for communication and model collaboration in future 6G intelligent transportation.
Advances in quantum computing present a critical challenge to current public-key cryptography, posing significant risks to the infrastructure of smart cities and intelligent transportation systems (ITS). This paper proposes QuanReS, a hybrid framework that integrates Quantum Key Distribution (QKD) with classical infrastructure to deliver quantum-resistant security for Vehicle-to-Everything (V2X) communications. We propose an SDN-based control layer that intelligently manages cryptographic resources by dynamically allocating secure QKD keys to protect high-sensitivity data in V2X communications, while employing post-quantum cryptographic schemes for less critical traffic. Through a comprehensive simulation for ITS applications, we demonstrate that this hybrid approach preserves the low-latency and high-throughput requirements essential for consumer electronics and real-time control systems, while ensuring strong pritection against quantum-enabled attacks. The results demonstrate that QuanReS achieves an effective balance between quantum resilience and practical deployment constraints, providing a scalable architectural framework for securing next-generation urban infrastructures.
Quantum computing is on the cusp of transforming the computational strength of “Intelligent Transportation Systems” (ITSs), but issues such as the scalability of qubits and error correction continue. With wireless networks shifting from 5G to 6G, technologies such as extended mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and the Internet of Things (IoT) will transform global connectivity. Though 5G offers improved speeds, it is prone to threats based on quantum computing, making quantum-safe cryptographic solutions for 6G networks essential. This paper discusses the revolutionary capability of quantum machine learning (QML) to meet the changing needs of future wireless systems. Through the use of quantum algorithms for data processing, QML can improve network performance, real-time decision-making, and increase security with quantum-safe cryptography. This paper sets out the challenges and prospects for the integration of quantum technologies into the design and deployment of secure, efficient, and scalable 6G wireless communication systems.
The rise of autonomous vehicles (AVs) has led to an increased reliance on vehicle-to-vehicle (V2V) communication networks to ensure real-time information sharing, situational awareness, and cooperative decision-making. However, the integration of quantum computing into cybersecurity threatens the cryptographic foundations upon which current V2V communication protocols rely. As quantum computers grow closer to practical implementation, traditional public-key encryption schemes—such as RSA and ECC—are rendered vulnerable to attacks from quantum algorithms like Shor’s and Grover’s. This review explores the necessity of adopting quantum-resistant cryptographic protocols to secure V2V communication frameworks. The study critically examines post-quantum cryptographic algorithms, including lattice-based, hash-based, code-based, multivariate polynomial, and isogeny-based schemes, with a focus on their applicability to latency-sensitive and resource-constrained vehicular environments. Furthermore, the paper evaluates the performance, scalability, and implementation challenges of integrating these cryptographic primitives into real-time autonomous systems. Case studies of pilot implementations and emerging research are reviewed to highlight the practicality of these protocols in vehicular edge computing and 5G-enabled automotive networks. The paper concludes by proposing a forward-looking roadmap for standardization and integration of quantum-resilient cryptography in intelligent transportation systems (ITS), ensuring long-term data integrity, authentication, and privacy across V2V infrastructures.
The emergence of sixth-generation (6G) wireless networks introduces unprecedented requirements for ultra-secure, low-latency communication across heterogeneous space–air–ground integrated network (SAGIN). Existing drone communication frameworks including LoRaWAN, Long Term Evolution, and Ad Hoc mesh architectures exhibit critical vulnerabilities to eavesdropping, jamming, and quantum-computational attacks due to their reliance on classical cryptographic primitives. To address these challenges, this work presents the Quantum-Secured Adaptive Routing Algorithm (QSARA), a novel framework designed for 6G-enabled unmanned aerial vehicle (UAV) networks that integrates Quantum Key Distribution (QKD), Reconfigurable Intelligent Surfaces (RIS), and Joint Communication and Sensing (JCAS) to enhance information-theoretic security and real-time performance. The proposed framework employs a quantum-augmented dynamic graph model to represent UAV swarm networking and uses Proximal Policy Optimisation (PPO)-based deep reinforcement learning to optimise routing under adversarial and uncertain conditions. A multi-objective cost function jointly captures classical quality of service metrics, such as latency, bandwidth, and energy consumption alongside with quantum-layer security indicators, including quantum bit error rate, key pool entropy, and key availability. High-fidelity simulations with 500 mobile drones under diverse adversarial threats demonstrate that the proposed framework achieves a key establishment success rate of 96.2%, end-to-end latency of 23.7 milliseconds, energy consumption of 7.8 watt-hours, and a packet delivery ratio of 94.1%, outperforming state-of-the-art classical and quantum-aware baselines. These results position the QSARA as a scalable and quantum-resilient routing solution for mission-critical UAV networking in next-generation 6G smart mobility ecosystems.
Immersive and mission-critical data-driven applications, such as virtual or augmented reality, tactile Internet, industrial automation, and autonomous mobility, are creating unprecedented challenges for ultra-reliable and low-latency communication (URLLC) in the sixth generation (6G) networks. Machine intelligence approaches deep learning, reinforcement learning, and federated learning (FL), to provide new paradigms to ensure 6G URLLC on the stream of big data training. However, classical limitations of machine learning capabilities make it challenging to achieve stringent 6G URLLC requirements. In this article, we investigate the potential of variational quantum computing and quantum machine learning (QML) for 6G URLLC by utilizing the advantage of quantum resources, such as superposition, entanglement, and quantum parallelism. The underlying idea is to integrate quantum machine intelligence with 6G networks to ensure stringent 6G URLLC requirements. As an example, we demonstrate the quantum approximate optimization algorithm for NP-hard URLLC task offloading optimization problems. The variational quantum computation for QML is also adopted in wireless networks to enhance the learning rate of machine intelligence and ensure the learning optimality for mission-critical applications. Considering the security and privacy issues, as well as computational-resource overheads in FL, distributed quantum computation in blind and remote fashions is further investigated for quantum-assisted FL.