Digital Twin-Aided Vehicular Edge Network: A Large-Scale Model Optimization by Quantum-DRL

This paper presents an innovative large model framework for optimizing the task offloading efficiency in vehicular edge networks, with a focus on ultra-reliable low-latency communication. We introduce a comprehensive model that integrates quantum computing with a deep reinforcement learning (DRL) model, supported by long short-term memory (LSTM) networks and a digital twin framework. This integration is designed to address the complexities of distributed vehicular edge computing networks, targeting efficient latency, energy, and quality-of-service management. Our model utilizes the parallel processing capabilities of quantum computing to enhance the DRL algorithm, effectively handling high-dimensional decision spaces. LSTM networks provide predictive insights into future network states in a digital twin framework and ensure real-time synchronization and adaptive strategy optimization. We employ a multi-agent framework, encompassing vehicles, unmanned aerial vehicles, and base stations, each utilizing a Nash equilibrium-based strategy for optimal decision-making, supplemented by incentive and penalty functions for reward optimization. Simulation results demonstrate notable improvements in task offloading efficiency, highlighting the model's efficacy over conventional DRL models.