Quantum Cloud Computing for AI Model Training

Abstract

As the transition to 6G networks accelerates, optimizing network performance and ensuring 
seamless connectivity becomes increasingly crucial. This paper explores the integration of 
Quantum-Enhanced Artificial Intelligence (QE-AI) for optimizing 6G networks, with a particular 
focus on leveraging cloud-based global education networks. We propose a framework that 
combines quantum computing's potential to solve complex optimization problems with AI's 
capability to analyze vast amounts of data and predict network behaviors. The framework aims to 
address key challenges in 6G network management, including latency, bandwidth allocation, and 
energy efficiency. By utilizing cloud-based education networks as a testbed, we demonstrate how 
QE-AI can enhance network performance, provide adaptive resource allocation, and support realtime decision-making. Our results highlight significant improvements in network efficiency and 
reliability, paving the way for advanced educational applications and global connectivity solutions. 
This research offers insights into the future of network optimization, emphasizing the 
transformative impact of quantum computing and AI in next-generation networks.