Unmanned aerial vehicles (UAVs) are a promising platform for flexible quantum communication, complementing fiber and satellite links. However, the performance of UAV-based entanglement distribution is limited by the dynamic atmospheric channel. Traditional statistical models, relying on static assumptions, cannot accurately represent real-time channel variations crucial for optimization. To address this, we propose an AI-driven adaptive prediction framework using the Patch Time Series Transformer (PatchTST) model to forecast UAV-to-ground quantum channel transmittance. Simulations demonstrate that our method accurately predicts dynamic transmittance fluctuations and outperforms standard recurrent neural network benchmarks in capturing relevant channel features. Additionally, it enables reliable estimation of key performance metrics like the BBM92 secure key rate. This work demonstrates an effective deep learning strategy for real-time channel prediction, enhancing adaptive optimization and robustness in UAV-based quantum entanglement distribution.
Unmanned aerial vehicles (UAVs) are a promising platform for flexible quantum communication, complementing fiber and satellite links. However, the performance of UAV-based entanglement distribution is limited by the dynamic atmospheric channel. Traditional statistical models, relying on static assumptions, cannot accurately represent real-time channel variations crucial for optimization. To address this, we propose an AI-driven adaptive prediction framework using the Patch Time Series Transformer (PatchTST) model to forecast UAV-to-ground quantum channel transmittance. Simulations demonstrate that our method accurately predicts dynamic transmittance fluctuations and outperforms standard recurrent neural network benchmarks in capturing relevant channel features. Additionally, it enables reliable estimation of key performance metrics like the BBM92 secure key rate. This work demonstrates an effective deep learning strategy for real-time channel prediction, enhancing adaptive optimization and robustness in UAV-based quantum entanglement distribution.
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.
Quantum cryptography is a promising technology that achieves unconditional security, which is essential to a wide range of sensitive applications. In contrast to optical fiber, the free-space optical (FSO) link is efficiently used as a quantum channel without affecting the polarization of transmitted photons. However, the FSO link has several impairments, such as atmospheric turbulence and pointing errors, which affect the performance of the quantum channel. This paper proposes a quantum key distribution (QKD) scheme that uses a time-bin entanglement protocol over the FSO channel that suffers from various channel impairments. Due to the interest in unmanned aerial vehicles (UAVs) and their usefulness for many social, internet-of-things (IoT), civil, and military applications, the proposed QKD-FSO system is integrated with the ground-to-UAV platform. Furthermore, variances in the position and orientation of the UAV are investigated using a tracking system. These variances are considered when evaluating the overall performance of the proposed integrated system. For this purpose, closed-form expressions are obtained for the system average symbol error rate (ASER) and outage probability. The Monte Carlo simulation is used to verify the validity of the proposed expressions. The system security is investigated assuming photon number splitting (PNS) attack. Moreover, the transmit power, time-bin number, and modulation order are optimized to maximize the raw and secret key rates. The results show that for 500 m link, ASER22 mrad and signal-to-noise ratio > 7.5 dB which leads SNRth < 2.5 dB and raw key rate maximized by adjusting the number of time bins according to the received SNR.
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.
In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment.
Satellite-based Quantum Key Distribution (QKD) has emerged as a promising solution for global secure communication beyond the limitations of terrestrial optical fiber networks. However, practical free-space optical (FSO) satellite channels are highly dynamic due to atmospheric turbulence, background radiation, weather variability, and finite transmission durations. Most existing models rely on static channel assumptions and asymptotic secret key rate (SKR) estimations, which do not accurately reflect real-time operational conditions. This paper proposes an AI-assisted real-time satellite Continuous Variable QKD (CV-QKD) simulator that integrates live weather data with machine learning- based hybrid noise prediction to model realistic channel behavior. The predicted noise parameters are incorporated into an adaptive QKD framework to dynamically compute both asymptotic and finite-size corrected SKR values. The system further performs day–night comparative analysis and 24-hour temporal simulation to study channel variations over time. All operational parameters, including predicted noise, adaptive SKR, and finite-size adjustments, are logged for reproducibility and performance evaluation. The proposed work implemented in the QKD simulator bridges the gap between theoretical QKD modeling and practical satellite deployment by introducing environmental awareness, AI-driven channel adaptation, and finite-size security considerations within an interactive visualization platform. This framework provides a scalable and realistic tool for analyzing next-generation satellite quantum communication systems.
Unmanned aerial vehicles (UAVs) enable rapidly deployable secure communication, while quantum key distribution (QKD) ensures information-theoretic security over free-space optical channels. In UAV-assisted QKD, however, atmospheric turbulence and mobility-induced misalignment introduce stochastic channel fluctuations that cause intermittent secrecy degradation. Existing trajectory optimization methods are largely deterministic or risk-neutral and fail to address rare but severe deep-fading events. We develop a risk-sensitive stochastic control framework for robust UAV-assisted free-space QKD under atmospheric uncertainty. The UAV motion is modeled as a continuous-time stochastic dynamical system, and the quantum channel transmissivity is characterized by mobility-dependent log-normal fading, yielding a stochastic secret key rate process. The exponential performance functional simultaneously penalizes the propulsion energy and secrecy outage, resulting in a nonlinear risk-sensitive Hamilton-Jacobi-Bellman equation. Simulation results show that there is a significant reduction in secrecy outage probability and key rate variation when atmospheric turbulence exists, but there is only a relatively minor rise in the energy consumption for propulsion, achieving a robustness-efficiency tradeoff for secure aerial quantum communication.