A Comprehensive Review of Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Models

Abstract

The rapid advancement of wireless communication technologies has accelerated the development of sixth-generation (6G) networks designed to provide ultra-high data rates, ultra-low latency, massive connectivity, and intelligent network automation. Efficient resource management has become a major challenge because of the dynamic, heterogeneous, and large-scale nature of 6G communication environments. Traditional optimization methods often struggle to meet the real-time processing and adaptability requirements of modern wireless networks. As a result, artificial intelligence and deep learning techniques have emerged as effective solutions for intelligent resource allocation, network optimization, load balancing, and energy-efficient communication management. This review presents a comprehensive analysis of efficient resource management in 6G communication networks using hybrid quantum duplet-convolutional neural network (CNN) models. Hybrid quantum deep learning frameworks combine the computational advantages of quantum computing with the feature extraction capabilities of classical CNN architectures, enabling improved decision-making, faster convergence, and enhanced optimization performance. The study examines AI-driven resource management techniques, reinforcement learning methods, quantum-enhanced optimization strategies, and hybrid deep learning architectures for future wireless communication systems. Emerging technologies such as federated learning, edge intelligence, and quantum machine learning are also discussed. The findings indicate that hybrid quantum-CNN models have strong potential for improving scalability, adaptability, and intelligent resource allocation in next-generation 6G networks while supporting efficient and reliable communication infrastructures.