The forthcoming 6G wireless systems must simultaneously achieve extreme capacity, ultra-low latency, and quantum-grade security. While classical physical-layer security techniques provide probabilistic protection, free-space quantum key distribution (QKD) suffers degradation under mobile and turbulent conditions. This paper introduces a Quantum-Enhanced Reconfigurable Intelligent Surface (QE-RIS) framework that synergistically integrates quantum photonics with classical electromagnetic control via hierarchical AI orchestration. Our design embeds quantum-capable devices into RIS unit cells operating at 12.5GHz (RF) and 850nm (optical) wavelengths and employs a dual-loop control mechanism, a fast loop at 5 ms and a mid loop at 50 ms, to dynamically select classical, quantum, or hybrid modes based on real-time signal-to-noise ratio (SNR) and quantum bit error rate (QBER). Monte Carlo simulations with 20 independent channel realisations demonstrate SNR gains up to 29.3 dB for RIS withN= 512 elements and 4-bit phase quantisation, QBER reductions ranging from 20% to 35% compared to free-space QKD, control latencies below 10 ms on edge-class hardware, and secure key rates exceeding 1 Mbit s−1under moderate turbulence. In this paper, we detail implementation assumptions, control information elements (IEs), and key performance indicators (KPIs) aligned with emerging standardisation efforts. The proposed framework offers a rigorous and reproducible foundation for advancing quantum-enhanced wireless communications.
The forthcoming 6G wireless systems must simultaneously achieve extreme capacity, ultra-low latency, and quantum-grade security. While classical physical-layer security techniques provide probabilistic protection, free-space quantum key distribution (QKD) suffers degradation under mobile and turbulent conditions. This paper introduces a Quantum-Enhanced Reconfigurable Intelligent Surface (QE-RIS) framework that synergistically integrates quantum photonics with classical electromagnetic control via hierarchical AI orchestration. Our design embeds quantum-capable devices into RIS unit cells operating at 12.5GHz (RF) and 850nm (optical) wavelengths and employs a dual-loop control mechanism, a fast loop at 5 ms and a mid loop at 50 ms, to dynamically select classical, quantum, or hybrid modes based on real-time signal-to-noise ratio (SNR) and quantum bit error rate (QBER). Monte Carlo simulations with 20 independent channel realisations demonstrate SNR gains up to 29.3 dB for RIS withN= 512 elements and 4-bit phase quantisation, QBER reductions ranging from 20% to 35% compared to free-space QKD, control latencies below 10 ms on edge-class hardware, and secure key rates exceeding 1 Mbit s−1under moderate turbulence. In this paper, we detail implementation assumptions, control information elements (IEs), and key performance indicators (KPIs) aligned with emerging standardisation efforts. The proposed framework offers a rigorous and reproducible foundation for advancing quantum-enhanced wireless communications.
This paper presents a novel system architecture for hybrid satellite communications, integrating quantum key distribution (QKD) and classical radio frequency (RF) data transmission using a dual-band reconfigurable intelligent surface (RIS). The motivation is to address the growing need for global, secure, and reliable communications by leveraging the security of quantum optical links and the robustness of classical RF channels within a unified framework. By employing a frequency-selective RIS, the system independently optimizes both quantum (850 nm) and classical (S-band) channels in real time, dynamically adapting to environmental fluctuations such as atmospheric turbulence and rain attenuation. The joint optimization of the quantum bit error rate (QBER) and the classical signal-to noise ratio (SNR) is formulated as a quadratic unconstrained binary optimization (QUBO) problem, enabling efficient adaptive phase control utilizing both quantum and classical computational methods. Comprehensive theoretical modeling and simulations, benchmarked against experimental data from the Micius satellite, demonstrate substantial performance gains. Notably, the RIS assisted system reduces QBER from approximately 2.5% to 0.7%, increases the secure key rate (SKR) to over 30,000 bits per second, and enhances classical RF SNR by about 3 dB at high elevation angles. These results illustrate the practical potential of hybrid RIS-assisted satellite links to deliver robust, efficient, and secure global communications.
Quantum-classical hybrid communication is emerging as a key enabler for future 6G networks, where THz communication and quantum services are expected to coexist over intelligent and reconfigurable wireless infrastructures. A fundamental challenge in such systems is that reconfigurable intelligent surface (RIS) phase control simultaneously influences classical communication performance and quantum-state evolution, making reliable entanglement preservation difficult in dynamic propagation environments. To address this challenge, this paper proposes an AI-native Entanglement-Sustained Reconfigurable Intelligent Surface (AI-ES-RIS) framework for hybrid quantum-classical THz communication. A unified model is developed to jointly characterize THz-wave propagation and Bell-state evolution through a phase-flip-inspired decoherence representation. To capture RIS-induced quantum degradation, a weighted mismatch-based decoherence model is introduced that links RIS phase perturbations, channel-dependent element significance, and entanglement fidelity degradation. Furthermore, a Proximal Policy Optimization (PPO)-based controller is designed to adapt RIS phase configurations by jointly optimizing communication reliability and quantum-state preservation. Simulation results demonstrate that the proposed framework consistently achieves higher entanglement fidelity, lower decoherence probability, and competitive communication performance compared with communication-centric and model-based RIS optimization strategies. Distributional analysis further confirms improved quantum-state robustness across diverse channel realizations, while scalability studies demonstrate favorable runtime characteristics for larger RIS dimensions. Additional meta-learning and federated learning extensions show enhanced adaptation and convergence under heterogeneous propagation environments. The results establish that RIS control in hybrid quantum-classical networks should be treated as a joint communication-and-quantum optimization problem rather than as independent design objectives, providing a promising foundation for future THz-enabled quantum-aware 6G systems.
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.
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.
ABSTRACT This paper presents a cybersecure hybrid quantum–classical architecture for synchronising distributed quantum neural networks (QNNs) in 6G holographic communications. The proposed framework targets secure, low‐latency and high‐fidelity end‐to‐end operation under realistic noise and adversarial conditions. Multipartite Greenberger–Horne–Zeilinger (GHZ) entanglement supports gradient‐consensus synchronisation, regularised by von Neumann entropy and trace distance. Holographic tensor teleportation is executed over reconfigurable intelligent surface (RIS)‐assisted midhaul links protected by quantum key distribution (QKD). Joint optimisation of QNN parameters and RIS phases targets high end‐to‐end fidelity under noise. Qiskit simulations averaged over 100 trials achieve , and , with 22.5 ms end‐to‐end latency. Compared with reduced baselines without GHZ synchronisation or without RIS control, fidelity improves by 20%–28% and synchronisation divergence decreases by about 90%. Scalability, security stress scenarios, classical‐feedback impairments, hyperparameter sensitivity and noisy intermediate‐scale quantum (NISQ)‐oriented error mitigation are all evaluated. The architecture scales gracefully to distributed units with only 7% fidelity reduction, whereas mitigation improves fidelity by 7%–11% under moderate noise. Overall, the RIS‐QKD‐GHZ integration enables secure, low‐latency and scalable quantum–classical 6G networking.
Sixth-generation (6G) wireless networks are expected to deliver transformative capabilities including ultra-reliable low-latency communications and massive connectivity. Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology by dynamically manipulating wireless propagation through programmable reflecting elements. However, accurate channel estimation in RIS-assisted networks faces significant challenges from cascaded channel structures, passive elements, and high-dimensional parameter spaces. Quantum machine learning (QML) offers a fundamentally different computational paradigm that may address these challenges through quantum properties. This article provides a tutorial overview of QML for channel estimation in RIS-assisted 6G networks. We present RIS-assisted system architectures and channel estimation challenges, introduce quantum computing fundamentals, and survey QML architectures. As a use case, we consider a hybrid quantum-classical framework, e.g., convolutional neural network (CNN)-quantum long short-term memory (QLSTM), for channel estimation in RIS-assisted non-orthogonal multiple access (NOMA) systems. This case study shows consistent accuracy improvements compared to quantum and classical benchmarks. Finally, we discuss implementation challenges and future research directions for practical deployment of quantum-enhanced wireless systems.