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

2026-05-18

AI Summary

This work addresses key challenges in 6G vehicle-to-everything (V2X) systems—namely, low communication efficiency, limited model generalization, and difficulties in coordinating heterogeneous nodes—by proposing the first edge-network framework that deeply integrates quantum machine learning. The architecture synergistically combines semantic communication, multimodal fusion, model transfer, and federated aggregation, and innovatively incorporates quantum convolutional neural networks, quantum attention mechanisms, quantum reinforcement learning, and quantum tensor decomposition to enable efficient collaboration. Experimental results demonstrate that the proposed approach significantly enhances communication efficiency, model generalization, and system robustness, while simultaneously preserving privacy and reducing the overhead of federated aggregation, thereby offering a novel paradigm for intelligent transportation in 6G networks.

Abstract

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.

Problem

• 6G
• V2X communication
• quantum machine learning
• model aggregation
• heterogeneous networks

Innovation

• Quantum Machine Learning
• Semantic Communication
• Multimodal Fusion
• Federated Aggregation
• Quantum Reinforcement Learning