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.
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.
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.
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.
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.
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.
Reconfigurable intelligent surface (RIS), which can manipulate the wireless environment, has recently been integrated into physical-layer key generation (PKG) systems to establish randomness symmetric keys in static environments. However, few studies have jointly considered the optimization and randomization of the RIS elements to simultaneously achieve a high key generation rate (KGR) and communication performance. This study proposes a RIS-assisted communication and security-integrated protocol for dual-function integration of communication enhancement and PKG in static environments. The protocol partitions RIS elements for beamforming and random beams for parallel execution, utilizing a sparsity adaptive matching pursuit-based channel estimation algorithm to obtain individual channel state information. Subsequently, an optimization problem is formulated for KGR maximization while satisfying quality of service (QoS) requirements. The non-convex optimization problem is addressed through monotonicity analysis and triangle inequality. We validate the efficacy of the proposed protocol by developing a 4.9 GHz RIS-assisted PKG prototype system, comprising modular hardware and flexible software. The field trials demonstrate a 30.81 bit/s KGR for static environments with an average received power increase of 10.6 dB, achieving effective simultaneous integration of communication and security.
The escalating complexity and stringent performance demands of sixth-generation wireless systems necessitate advanced signal processing methods capable of simultaneously achieving high spectral efficiency and low computational complexity, especially under frequency-selective propagation conditions. In this paper, we propose a hybrid quantum-classical detection framework for broadband systems enhanced by reconfigurable intelligent surfaces (RISs). We address the maximum likelihood detection (MLD) problem for RIS-aided broadband wireless communications by formulating it as a quadratic unconstrained binary optimization problem, that is then solved using Grover adaptive search (GAS). To accelerate convergence, we initialize the GAS algorithm with a threshold based on a classical minimum mean-squared error detector. The simulation results show that the proposed hybrid classical-quantum detection scheme achieves near-optimal MLD performance while substantially reducing query complexity. These findings highlight the potential of quantum-enhanced detection strategies combined with RIS technology, offering efficient and near-optimal solutions for broadband wireless communications.