Quantum networks connect devices to facilitate the transmission of quantum bits (qubits), harnessing quantum mechanics for groundbreaking telecommunications applications. The quantum state exchange rely on quantum entangled particles which have to be sent from a Quantum Base Station (QBS) to Quantum Nodes (QNs) through an optical channel. Free Space Optical (FSO) links promise flexibility and cost advantages over the classical fiber-based infrastructure. However, the absence of Line of Sight (LoS) between the QBS and QNs may completely disrupt the communication. Therefore, this work focuses on an Intelligent Reflective Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV)-aided FSO quantum network. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV hovering locations, constrained by fidelity and link-per-IRS limits. The intractability of the original problem is first tackled with a sigmoid-based approximations, subsequently split into three sub-problems, sequentially solved applying the Successive Convex Approximation (SCA) technique. Numerical results (i) validate the approximation accuracy, and (ii) demonstrates the effectiveness of the proposal against three baseline approaches, showing an order of magnitude increase in terms of fairness.
Quantum networks connect devices to facilitate the transmission of quantum bits (qubits), harnessing quantum mechanics for groundbreaking telecommunications applications. The quantum state exchange rely on quantum entangled particles which have to be sent from a Quantum Base Station (QBS) to Quantum Nodes (QNs) through an optical channel. Free Space Optical (FSO) links promise flexibility and cost advantages over the classical fiber-based infrastructure. However, the absence of Line of Sight (LoS) between the QBS and QNs may completely disrupt the communication. Therefore, this work focuses on an Intelligent Reflective Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV)-aided FSO quantum network. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV hovering locations, constrained by fidelity and link-per-IRS limits. The intractability of the original problem is first tackled with a sigmoid-based approximations, subsequently split into three sub-problems, sequentially solved applying the Successive Convex Approximation (SCA) technique. Numerical results (i) validate the approximation accuracy, and (ii) demonstrates the effectiveness of the proposal against three baseline approaches, showing an order of magnitude increase in terms of fairness.
Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users’ quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 84% in users’ end-to-end fidelities. The proposed framework also achieves a 63% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence.
This paper studies intelligent reflecting surface (IRS)-assisted free space optical (FSO) quantum communication systems. An IRS can relax the requirement of a line-of-sight (LoS) link between Alice and Bob for FSO quantum key distribution (QKD) applications. The IRS focuses the laser beam toward Bob by introducing a phase shift to the incoming incident beam from Alice. Both discrete variable QKD (DV-QKD) and continuous variable QKD (CV-QKD) protocols for IRS-assisted QKD systems are studied. For DV-QKD, we characterize the average quantum bit error rate (QBER) and the achievable secret key rate (SKR) under a log-normal fading turbulence model. For CV-QKD, we present the ultimate quantum information-theoretic bounds for the SKR and entanglement distribution rates. We also characterize the achievable SKR of a practical CV-QKD protocol implemented using Gaussian coherent states. Our results reveal that Alice and Bob can generate a quantum secure key even if the LoS link between them is blocked by utilizing the IRS. Furthermore, with a quadratic phase shift profile, the IRS-assisted link achieves a higher SKR as compared to the direct LoS link and a relay-assisted link for large transmission distances.
This letter investigates a continuous-variable quantum key distribution (CV-QKD) protocol based on Gaussian-modulated coherent states over unmanned aerial vehicle (UAV)-based free-space optical (FSO) links. A realistic fading channel model is developed, including atmospheric turbulence, extinction, and UAV-induced pointing errors. We perform distance-dependent modulation variance optimization to maximize the secret key rate (SKR) under three scenarios: asymptotic, finite-block size (FBS), and FBS with adaptive optics (AO), based on numerical simulations incorporating a realistic UAV-FSO channel model. The results show that finite-size effects reduce the secure transmission range to ~800 m, while AO extends it to ~930 m. Furthermore, optimal modulation improves key rates by up to 40% compared to fixed modulation. This is the first work to jointly model UAV-FSO channel fluctuations, optimize modulation, and quantify finite-size security with AO, confirming the feasibility of CV-QKD for UAV-based communications.
Unmanned aerial vehicles (UAVs)-based free-space optics (FSO)/quantum key distribution (QKD) systems require high-precision pointing mechanisms. This increases system complexity and limits rapid deployment for lightweight and energy-constrained UAVs. This paper proposes a modulating retroreflector (MRR)-equipped UAV architecture for BB84-QKD systems that enables simplified yet accurate tracking while relaxing pointing requirements. A realistic quantum channel model is developed, for which we newly derive the channel probability distribution of transmittance (PDT). Capitalizing on the derived channel PDT, several QKD performance metrics are analytically obtained. Numerical results verify the feasibility of the proposed MRR-aided UAV for practical QKD deployment, highlight its effectiveness over conventional UAV-ground systems, and validate the accuracy of the developed analytical framework.
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.