Cybersecure Synchronisation of Entangled Quantum Neural Networks With Reconfigurable Intelligent Surface and Quantum Key Distribution for 6G Holographic Communications
Raad S. Alhumaima, Yassir Al-Karawi, Hamed Saffa Al-Raweshidy
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.
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.
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-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.
With the ever-increasing proliferation of Internet of Things (IoT) devices and advent in quantum computing, the need for quantum-secure cryptographic protocols is becoming more and more critical. This paper introduces a transformative approach to Quantum Key Distribution (QKD), integrating Quantum Neural Networks (QNNs) and adaptive entanglement protocols for securing, scaling, and rendering IoT communications resilient. The proposed architecture is scalable, hardware-efficient, and noise-resilient, enabling AI-assisted protocol optimization, dynamic qubit allocation, and QNN-based noise mitigation. Simulations with Qiskit and PennyLane produce a fidelity of 99.9% and achieve an average QBER of 0.015, representing a 32% reduction compared to the 0.022 QBER typically reported for Measurement-Device-Independent-QKD (MDI-QKD). The proposed QNN model classifies with an accuracy of 97.5%, significantly outperforming the VQNN’s 80.5%. A hierarchical quantum authentication scheme is also proposed to ensure multilayer security in heterogeneous IoT settings.
ABSTRACT Hybrid quantum networks will be the communication backbone of the future quantum internet, linking together heterogeneous discrete‐variable (DV) and continuous‐variable (CV) quantum systems. However, current quantum key distribution protocols assume a static basis and are not designed for dynamically changing hybrid quantum environments. This paper suggests an adaptive machine learning‐assisted teleportation quantum key distribution framework that integrates the following steps into a quantum‐classical architecture: hybrid DV–CV communication, quantum teleportation, adaptive basis selection and neural‐assisted quantum error correction. Optimal measurement basis prediction, by means of a supervised multilayer perceptron (MLP), using six real‐time channel descriptors, allows for intelligent protocol adaptation under heterogeneous channel conditions. The framework proposed was realized in Qiskit Aer and Scikit‐learn and tested with 6000 different realisations of the different channels. The classification accuracy was 99.44% while the average inference latency was only 0.0011 ms per channel sample, making it possible to use the adaptive basis selector in real time. The teleportation fidelity of AMT‐QKD was 0.899 at a representative channel noise level of 10%, with a quantum bit error rate (QBER) of 0.088, a secret key rate of 0.404 and an eavesdropper mutual information of only . They are shown to be more robust, have less information leakage, and be more secure in generating keys under realistic channel impairments when compared to other protocols, including BB84, Enhanced BB84, B92, BBM92, E91 and CV‐QKD. The findings provide a robust framework for the deployment of AMT‐QKD in secure communication in future hybrid quantum networks and quantum internet applications.
The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
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.