Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment

This study introduces a quantum-inspired framework for optimizing the exploration-exploitation tradeoff in multi-agent reinforcement learning (MARL), specifically applied to UAV-assisted 6G network deployment. The framework involves ten intelligent UAVs cooperatively coordinating to maximize signal coverage and efficiently expand the network under partial observability and dynamic conditions. It integrates classical MARL algorithms with quantum-inspired optimization techniques, utilizing variational quantum circuits (VQCs) and the Quantum Approximate Optimization Algorithm (QAOA). The approach also incorporates probabilistic modeling (Bayesian inference, Gaussian processes) to capture environmental dynamics. Experimental results show superior performance in exploration efficiency, coverage rate, and convergence speed compared to classical MARL methods, offering a robust solution for next-generation communication network optimization.