Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE Vehicular Technology Magazine

Quantum Machine Learning-Based 6G Network: Enabling Adaptive Communication and Model Aggregation

Wenjing Xiao, Jiatai Yan, Chenglong Shi, S S Chen, Miaojiang Chen, Min Chen, Saif M. Al-Kuwari, Ahmed Farouk

Published Apr 29, 2026• 1 citation
Publisher
DOI
10.1109/mvt.2026.3680174

Related papers

A Hybrid Machine Learning Framework for Adaptive Quantum Communication Networks

M. Ramu, K. Dora Babu, T. Aditya Sai Srinivas +3 more

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)•Oct 8, 2025•0 citations

Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation

Wenjing Xiao, Jiatai Yan, Chenglong Shi +5 more

With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and model collaboration. Conventional machine learning struggles with high-dimensional state spaces, slow convergence, and poor generalization under heterogeneous V2X nodes, rapidly varying channels, and multimodal sensing data in V2X systems. To address these issues, we propose a quantum-enhanced framework for V2X communication and model aggregation that targets efficient, robust, and intelligent transportation in 6G, which includes four modules: the channel-adaptive semantic communication module, the multimodal fusion module, the model transfer module, and the federated aggregation module. Specifically, the channel-adaptive semantic communication module leverages quantum convolutional neural networks (CNN) and quantum distortion metrics to enable efficient transmission and strong generalization across diverse conditions. The multimodal fusion module exploits quantum attention and entanglement to compress features and associate semantics across heterogeneous data. The model transfer module employs quantum reinforcement learning to model decision-making and improve adaptability in dynamic environments. The federated aggregation module integrates quantum tensor decomposition with backpropagation-based corrections to provide privacy preservation with low overhead and to strengthen global model robustness. This work outlines a new paradigm for communication and model collaboration in future 6G intelligent transportation.

quant-pharXiv:2605.27417•May 19, 2026•0 citations

Quantum Machine Learning with Adaptive Quantum Networks

Christopher Altman

•Jul 17, 2026•0 citations

QFMLNet: An AI-Driven Quantum Communication Architecture for 6G Space-Ground Networks

Xufei Ding, Yingchi Mao, Jianan Huang +3 more

Quantum communication in 6G offers unprecedented data transmission speeds, ultra-low latency, and seamless space-ground connectivity. AI and quantum communication technologies, including quantum machine learning (QML) and quantum federated learning (QFL), play a key role in enhancing secure communication and data processing despite challenges like high resource demands and unique security risks. To overcome these challenges, we propose QFMLNet, a dedicated framework that enables a decentralized quantum neural network (QNN) training on protected data. QFMLNet is a secure and communication-efficient framework that combines quantum homomorphic encryption and quantum key distribution as its security mechanism. It enables the edge to train its private QNN model and transmit it to the server for aggregation through quantum homomorphic encryption. Each framework layer is designed to address specific needs in 6G quantum networks, enhancing model robustness, safeguarding communication, and ensuring reliable performance in complex, security-sensitive environments. The framework enhances model robustness and safeguards communication, offering a structured solution for secure quantum communication in 6G space-ground networks.

IEEE Communications Magazine•Jun 27, 2025•0 citations

Artificial Intelligence Techniques for Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model: Trends and Challenges

Leocadia Nithisarn

The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.

Multidisciplinary Journal of Research in Engineering and Technology•Aug 19, 2024•0 citations

Deep quantum neural networks AI for 6G/7G communication systems

Abhishek Kumar, Pramod Singh ca. 20./21. Jh. Rathore, Reyes ca. 20./21. Jh. Juárez-Ramírez

•Jan 1, 2026•0 citations
With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and model collaboration. Conventional machine learning struggles with high-dimensional state spaces, slow convergence, and poor generalization under heterogeneous V2X nodes, rapidly varying channels, and multimodal sensing data in V2X systems. To address these issues, we propose a quantum-enhanced framework for V2X communication and model aggregation that targets efficient, robust, and intelligent transportation in 6G, which includes four modules: the channel-adaptive semantic communication module, the multimodal fusion module, the model transfer module, and the federated aggregation module. Specifically, the channel-adaptive semantic communication module leverages quantum convolutional neural networks (CNN) and quantum distortion metrics to enable efficient transmission and strong generalization across diverse conditions. The multimodal fusion module exploits quantum attention and entanglement to compress features and associate semantics across heterogeneous data. The model transfer module employs quantum reinforcement learning to model decision-making and improve adaptability in dynamic environments. The federated aggregation module integrates quantum tensor decomposition with backpropagation-based corrections to provide privacy preservation with low overhead and to strengthen global model robustness. This work outlines a new paradigm for communication and model collaboration in future 6G intelligent transportation.
Quantum communication in 6G offers unprecedented data transmission speeds, ultra-low latency, and seamless space-ground connectivity. AI and quantum communication technologies, including quantum machine learning (QML) and quantum federated learning (QFL), play a key role in enhancing secure communication and data processing despite challenges like high resource demands and unique security risks. To overcome these challenges, we propose QFMLNet, a dedicated framework that enables a decentralized quantum neural network (QNN) training on protected data. QFMLNet is a secure and communication-efficient framework that combines quantum homomorphic encryption and quantum key distribution as its security mechanism. It enables the edge to train its private QNN model and transmit it to the server for aggregation through quantum homomorphic encryption. Each framework layer is designed to address specific needs in 6G quantum networks, enhancing model robustness, safeguarding communication, and ensuring reliable performance in complex, security-sensitive environments. The framework enhances model robustness and safeguards communication, offering a structured solution for secure quantum communication in 6G space-ground networks.
The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.