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.
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.
Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.
Quantum networks (QNs) supported by terahertz (THz) wireless links present a transformative alternative to fiber-based infrastructures, particularly in mobile and infrastructure-scarce environments. However, signal attenuation, molecular absorption, and severe propagation losses in THz channels pose significant challenges to reliable quantum state transmission and entanglement distribution. To overcome these limitations, we propose a dynamic reconfigurable intelligent surface (RIS)-assisted wireless QN architecture that leverages adaptive RIS elements capable of switching between active and passive modes based on the incident signal-to-noise ratio (SNR). These dynamic RIS elements enhance beamforming control over amplitude and phase, enabling robust redirection and compensation for THz-specific impairments through real-time measurement-driven adjustments. We develop a detailed analytical model that incorporates key physical layer phenomena in THz quantum links, including path loss, fading, thermal noise, and alignment variations. This model also acts as a digital twin, simulating quantum channel measurements under uncertainty. A secure optimization framework is formulated to jointly determine RIS placement and entanglement generation rate (EGR) allocation, while satisfying fidelity, security, and fairness constraints under diverse quality of service (QoS) demands [1] . Fidelity and leakage metrics are treated as measurable quantities, reinforcing the role of measurement in system adaptation. Simulation results demonstrate that the proposed architecture yields up to 87% fidelity enhancement and 65% fairness improvement compared to static RIS baselines, while maintaining robustness under realistic THz channel conditions. The integration of adaptive RIS control and fidelity-aware modeling highlights the digitalization of quantum measurement processes. These results underscore the promise of dynamic RIS technology in enabling scalable and adaptive quantum communications over wireless THz links.
This study proposes the utilization of quantum machine learning (QML) to maximize the energy efficiency of reconfigurable intelligent surface (RIS) assisted communication with rate-splitting multiple access (RSMA). The next-generation wireless communications are expected to yield significantly higher energy efficiency compared to that of the previous generations. In a multiuser system, energy efficiency can be defined as a benefit-to-cost ratio between the achievable sum-rate and the energy consumption, where enhancements in the former come at the expense of increases in the latter. Recently, the integration between RSMA and RISs has been advocated as a powerful mean to control this tradeoff. Indeed, RSMA can enhance the rate region while RISs can lead to reduced energy consumption thanks to the use of low-energy phase shifters. However, optimizing a RIS-aided RSMA communication system is faced with a computational burden given that the RIS enlarges the volume of the required channel information, which expands the information that needs to be processed by the optimization module, even when the optimization is based on conventional learning techniques. The proposed QML optimization framework, which orchestrates non-linear quantum unitary operations to compose the learning models, enjoys information processing gains thanks to state vector operations in multi-dimensional Hilbert space. It is composed of two trainable quantum-based learning models employed in an alternating manner: the first establishes the transmission precoding, and the second designs the RIS phase shifting. Numerical results show that the proposed QML delivers comparable performance to that of conventional optimization but with reduced complexity.
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 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.
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.