Artificial Intelligence Techniques for Efficient Resource Management in 6G Communication Networks Using a Hybrid Quantum Duplet-Convolutional Neural Network Model: Trends and Challenges

Abstract

The emergence of sixth-generation (6G) communication networks is expected to revolutionize wireless systems by enabling ultra-high data rates, ultra-low latency, and massive connectivity. Efficient resource management is a critical challenge due to the dynamic and heterogeneous nature of 6G environments. Traditional optimization techniques are insufficient to handle the complexity of such networks, leading to the adoption of artificial intelligence (AI)-driven approaches. This paper presents a comprehensive review of AI techniques for efficient resource management in 6G networks, focusing on hybrid quantum duplet-convolutional neural network (CNN) models. These models integrate quantum computing with classical deep learning to enhance computational efficiency and optimization capability. The study analyzes recent literature, covering deep learning, reinforcement learning, federated learning, and quantum machine learning approaches. A comparative analysis highlights the advantages of hybrid quantum-CNN models in terms of scalability, adaptability, and performance. The paper also discusses emerging trends such as AI-native network design, edge intelligence, and quantum-enhanced optimization. Furthermore, key challenges including computational complexity, energy consumption, and quantum hardware limitations are examined. The findings suggest that hybrid quantum deep learning models are promising for addressing resource management challenges in future 6G networks.