A Survey of Methods and Architectures for Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model

Abstract

The evolution toward sixth-generation (6G) communication networks introduces significant challenges in resource management due to ultra-dense connectivity, heterogeneous architectures, and stringent performance requirements such as ultra-low latency and high reliability. Traditional optimization-based approaches are inadequate for handling the highly dynamic and complex nature of these networks, leading to the growing adoption of Artificial Intelligence (AI), deep learning, and quantum computing techniques. This paper presents a comprehensive survey of methods and architectures for efficient resource management in 6G systems, with a particular focus on hybrid quantum duplet-convolutional neural network (HQD-CNN) models. Various approaches, including optimization techniques, machine learning, deep learning, and reinforcement learning frameworks, are examined to highlight their effectiveness in addressing network challenges. The findings reveal that AI-driven models significantly enhance performance through adaptive and intelligent decision-making, especially in dynamic resource allocation, network slicing, and interference management. Hybrid quantum deep learning models further improve efficiency by leveraging quantum parallelism to solve complex optimization problems. However, challenges such as computational complexity, scalability limitations, quantum hardware constraints, and security issues persist, indicating the need for scalable, secure, and AI-native solutions.