ABSTRACT Quantum communication represents a cutting‐edge research frontier, yet its wide‐area deployment remains constrained by infrastructure limitations. Unmanned aerial vehicle (UAV) technology, with its inherent flexibility and rapid deployability, offers a promising solution for dynamic quantum network expansion, giving rise to the emerging paradigm of UAV quantum networks. However, these networks face critical challenges, including energy and quantum resource constraints, as well as fragile quantum links. To address these issues, we propose an efficient clustering‐based communication scheme and develop an Energy‐Neighbor Weight Adaptive Clustering Algorithm (ENW‐ACA). This algorithm incorporates a multi‐index cluster head election strategy and a multi‐level routing mechanism to achieve low energy dissipation, high quantum resource utilization, and improved network throughput. Simulation results demonstrate that ENW‐ACA significantly outperforms traditional non‐clustering routing across multiple performance metrics, including energy efficiency, quantum resource utilization, and network throughput, while maintaining its advantages as the network scale expands. These findings provide theoretical support for clustering and routing design in UAV quantum networks and establish a technical foundation for their large‐scale deployment.
ABSTRACT Quantum communication represents a cutting‐edge research frontier, yet its wide‐area deployment remains constrained by infrastructure limitations. Unmanned aerial vehicle (UAV) technology, with its inherent flexibility and rapid deployability, offers a promising solution for dynamic quantum network expansion, giving rise to the emerging paradigm of UAV quantum networks. However, these networks face critical challenges, including energy and quantum resource constraints, as well as fragile quantum links. To address these issues, we propose an efficient clustering‐based communication scheme and develop an Energy‐Neighbor Weight Adaptive Clustering Algorithm (ENW‐ACA). This algorithm incorporates a multi‐index cluster head election strategy and a multi‐level routing mechanism to achieve low energy dissipation, high quantum resource utilization, and improved network throughput. Simulation results demonstrate that ENW‐ACA significantly outperforms traditional non‐clustering routing across multiple performance metrics, including energy efficiency, quantum resource utilization, and network throughput, while maintaining its advantages as the network scale expands. These findings provide theoretical support for clustering and routing design in UAV quantum networks and establish a technical foundation for their large‐scale deployment.
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.
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.
Abstract Unmanned aerial vehicles (UAVs) emerge as a flexible and cost‐effective solution for mobile quantum communication, showing significant potential. Despite the challenges posed by environmental noise in free‐space channels for efficient entanglement distribution, the noise‐resistant properties of high‐dimensional (HD) quantum states provide a promising solution to these issues. This work establishes a HD orbital angular momentum (OAM) entanglement distribution model on a UAV platform, systematically analyzing the effects of environmental conditions and platform parameters. Based on this model, a HD subspace coding protocol is investigated, with optimized subspace dimension and mode sets to enhance the performance. Furthermore, the use of the source modulation technique can significantly improve the key rate and transmission distance of the system. This work holds important implications for expanding UAV platform coverage, improving adaptability to environmental factors, and advancing the development of heterogeneous quantum networks.
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.
ABSTRACT This research presents a novel quantum‐assisted optimization framework for unmanned aerial vehicle‐assisted reconfigurable intelligent surface networks. It introduces a breakthrough metric known as quantum signal reconfiguration dynamics. Utilizing the principles of quantum computing, this framework enables real‐time signal propagation, optimizes resource usage, and reduces energy consumption in dynamic and complex scenarios. The integration of quantum algorithms significantly improves signal reconfiguration by allowing the resource allocator to coordinate control over reconfigurable intelligent surface elements effectively and make real‐time adjustments. This leads to enhanced signal strength, reduced interference, and lower overall energy consumption, minimizing the power requirements of the system. The framework employs optimization methods based on quantum computing, including the quantum approximate optimization algorithm and the variational quantum eigensolver, which can be adapted to the current network conditions. Stress‐testing using a computationally optimized fixed‐point algorithm demonstrates a threefold increase in signal reconfiguration dynamics, a fortyfold reduction in computational latency, and a 25% increase in coverage area compared to classical methods. These results represent significant progress in integrating reconfigurable intelligent surfaces with unmanned aerial vehicle‐assisted wireless communication networks. The proposed framework establishes a foundation for introducing much‐needed improvements to future communication architectures, particularly for sixth‐generation networks and beyond, promoting the development of intelligent and efficient communication systems.
Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience.