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 transformative evolution in wireless systems, offering ultra-high data rates, ultra-low latency, and massive device connectivity. However, these advancements also introduce significant challenges in efficient resource management, including spectrum allocation, energy optimization, network slicing, and latency control in highly dynamic and heterogeneous environments. Traditional optimization methods often struggle to adapt to such complexity, leading to the adoption of Artificial Intelligence (AI)-driven solutions. This paper presents a systematic review of recent advancements in resource management for 6G networks, with a focus on hybrid quantum duplet-convolutional neural network (HQD-CNN) models. These models combine quantum computing principles with convolutional neural networks to enhance computational efficiency, pattern recognition, and optimization performance. The review highlights key trends such as reinforcement learning-based resource allocation, hybrid CNN-LSTM architectures for dynamic network slicing, and quantum-inspired optimization techniques. Comparative analysis indicates that these hybrid approaches significantly improve Quality of Service (QoS) and Quality of Experience (QoE). Challenges including computational complexity, quantum hardware limitations, and data privacy are also discussed, along with future directions like federated learning and explainable AI.