Recent Advances in Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model: A Systematic Review

Abstract

The emergence of sixth-generation (6G) communication networks marks a significant advancement in wireless technology by enabling ultra-high data rates, ultra-low latency, massive connectivity, and intelligent network services. These capabilities introduce complex resource management challenges involving spectrum allocation, energy efficiency, network slicing, and dynamic traffic optimization, which conventional optimization techniques struggle to address due to the heterogeneous and highly dynamic nature of 6G environments. As a result, artificial intelligence, particularly deep learning and quantum-inspired computing, has gained considerable attention for developing adaptive and efficient resource management strategies. This systematic review examines recent advances in hybrid Quantum Duplet-Convolutional Neural Network (HQD-CNN) models that integrate quantum computing concepts with convolutional neural networks to improve computational efficiency, feature extraction, and optimization performance. The review highlights significant developments in reinforcement learning-based resource allocation, CNN-based hybrid architectures, and quantum-assisted optimization techniques that enhance throughput, reduce latency, improve energy efficiency, and support scalable network management. Comparative findings indicate that hybrid AI models consistently outperform conventional machine learning methods in maintaining Quality of Service (QoS) and Quality of Experience (QoE) across complex 6G scenarios. The review also discusses existing challenges, including computational complexity, limited quantum hardware, data privacy, and model interpretability, while identifying future research opportunities in federated learning, explainable AI, and scalable quantum-assisted intelligent resource management for next-generation wireless communication networks.