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.
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.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.
Unmanned Aerial Vehicles (UAVs), as key enablers of 6G-enabled Internet of Things (IoT) ecosystems, facilitate dynamic aerial coverage, seamless edge intelligence, and adaptive routing. However, despite these advantages, the reliability and trustworthiness of UAV swarms in 6G remain critical concerns due to rising quantum threats and limitations of traditional machine learning approaches. The paper presents QuaRTA-6G, a Quantum-Resilient, Trust-aware, and Accountable framework that provides a unified solution for 6G UAV swarms. QuaRTA-6G achieves security, trust, and efficiency by seamlessly combining post-quantum cryptography for secure communication, a decentralized ledger for identity and trust management, and Variational Quantum Federated Learning (VQFL) for efficient swarm intelligence. For quantum-resistant authentication and immutable UAV identity verification, QuaRTA-6G leverages CRYSTALS-Kyber with blockchain. Through simulations, QuaRTA-6G achieves secure authentication handshakes in under 2 ms and scales robustly to swarms of more than 100 UAVs, keeping high integrity even under 20% packet loss. Experiments demonstrate that, even under strong data poisoning attacks, the framework achieves 92% accuracy and 85% mission success rate, while comprehensive resource analysis confirms its feasibility across both standard and resource-constrained UAV platforms. Furthermore, an ablation study demonstrates that each module of QuaRTA-6G is essential for ensuring a responsible and trustworthy 6G UAV framework.
The emergence of 6G networks precipitates unprecedented challenges in securing multi-UAV spectrum sharing due to increased connectivity demands and exposure to sophisticated cyber threats. This research proposes a quantum-enabled blockchain protocol tailored for secure, decentralized spectrum management among multiple unmanned aerial vehicles (UAVs) in 6G environments. Leveraging quantum key distribution (QKD) integrated within a blockchain framework, the protocol ensures information-theoretic security while maintaining transparency and immutability of spectrum allocation records. The protocol incorporates consensus mechanisms optimized for low-latency UAV networks, enabling rapid transaction validation and conflict resolution. Simulation results indicate a substantial reduction in communication overhead and vulnerability to malicious spectrum hijacking, achieving over 95% detection accuracy of unauthorized access attempts. This hybrid cryptographic architecture addresses the dual challenges of scalability and security, paving the way for resilient spectrum sharing in UAV swarms under 6G paradigms.
The swift implementation of unmanned aerial ve- hicle (UAV) corridors in sixth-generation (6G) networks necessi- tates safe, intelligence-driven integrated sensing and communications (ISAC). Reconfigurable intelligent surfaces (RIS) improve spectrum efficiency, localisation precision, and situational awareness, while also introducing new vulnerabilities. The emergence of quantum computing heightens hazards associated with harvest-now, decrypt-later tactics and quantum-enhanced spoofing. We propose a quantum-resilient threat modelling (QRTM) framework for RIS-assisted ISAC in UAV corridors to tackle these problems. QRTM integrates classical, quantum-ready, and quantum-aided adversaries, addressing them with post-quantum cryptographic (PQC) primitives: ML-KEM for key establishment and Falcon for authentication, both incorporated inside RIS control signalling and UAV coordination. To enhance security sensing, we present RIS-coded scene watermarking validated by a generalised likelihood ratio test (GLRT), with its detection probability characterised by a Marcum-Q function. Additionally, we establish a secure ISAC utility (SIU) that concurrently optimises secrecy rate, spoofing detection, and throughput within RIS limitations, facilitated by a scheduler withO(n2)complexity. Monte Carlo evaluations utilising 3GPP Release-19 mid-band urban-canyon models(7−15G H z)reveal spoof-detection probability approaching 0.99 atPFA =10−3, secrecy-rate retention surpassing90%versus quantum-capable adversaries, and signal interference utilisation enhancements of around 25% relative to baselines. These findings underscore a standards-compliant approach to establishing a reliable, quantum-resilient ISAC for UAV corridors in smart cities and non-terrestrial networks.
This paper proposes a quantum-secured, fully distributed coordination framework for unmanned aerial vehicle (UAV) swarms that integrates distributed cluster GHZ (DC-GHZ) quantum key distribution, Hamiltonian continuous-time quantum walk (CTQW) routing, distributed average consensus, and constrained 3D kinematics within a single closed loop. DC-GHZ keying partitions the swarm into entanglement clusters, generates fresh symmetric keys each epoch, and monitors quantum bit error rate (QBER) as an intrinsic spoofing and eavesdrop detection signal. CTQW is applied over a waypoint graph whose potential encodes a multi-objective cost field combining distance, threat intensity, and congestion, while altitude is selected via separation-risk minimization and UAV motion is updated under bounded velocity, acceleration, and climb-rate limits. Our simulation shows that the quantum layer achieves mean QBER values of 0.1152 and 0.1089 across two GHZ clusters and raises intrusion alarms in seven of eight epochs, whereas the routing layer maintains stable average costs in the range 0.2771-0.3740 and the consensus process drives variance to near 10–3after initial transients. The results demonstrate that the proposed architecture can simultaneously provide quantum-layer intrusion awareness, stealthy multi-objective routing, collision-aware 3D mobility, and robust decentralized coordination, making it a viable candidate for secure and scalable UAV swarm operations in contested environments.
The proliferation of quantum key distribution (QKD) systems on unmanned aerial vehicles (UAVs) has demonstrated the technical feasibility of mobile quantum networks, yet practical deployment faces critical challenges in scalability, resource allocation, and network reliability. We present Q-MESH (Quantum Multi-tier Entanglement-based Scalable Hierarchy), a novel three-tier hierarchical architecture that leverages heterogeneous UAV swarms to establish metropolitan-scale quantum networks with unprecedented flexibility and fault tolerance. Our architecture introduces dynamic layer transitions, intelligent quantum routing protocols based on fidelity-aware graph theory, and adaptive resource allocation using reinforcement learning. Through comprehensive mathematical analysis and simulation, we demonstrate that Q-MESH achieves 3.7Ö higher effective key generation rates compared to flat network topologies while maintaining 99.2% uptime through autonomous reconfiguration. The architecture supports concurrent operation of 15-25 UAVs across three altitude tiers (0.5 km, 8 km, 18 km), providing continuous coverage over 150 km² metropolitan areas with average end-to-end key rates of 2.8 kbit/s and worst-case latency under 500 ms. Our innovation lies in the integration of adaptive optics coordination, turbulence-aware routing, and quantum memory-free relay protocols that collectively enable practical deployment of aerial quantum networks. Q-MESH represents a critical step toward ubiquitous quantumsecure communications infrastructure.