Dependency-Aware Task Offloading via Quantum Graph Attention Network-based Deep Reinforcement Learning in Collaborative Edge-Cloud Systems
Abstract
Edge–cloud collaboration has become essential for managing the limited computational capacity of edge servers while meeting the dynamic and latency-critical demands of wireless devices in 5G and beyond networks. However, efficient offloading of interdependent tasks remains challenging due to heterogeneous computing resources, time-varying wireless channels, and complex dependency structures among subtasks. This paper proposes a Quantum Graph Attention Network-based Deep Reinforcement Learning (QGAT-DRL) framework for dependency-aware task offloading in collaborative edge–cloud systems. Computation tasks generated by user equipments (UEs) are represented as directed acyclic graphs (DAGs) to capture subtask dependencies, while a quantum-enhanced graph attention mechanism exploits superposition and entanglement to encode high-order correlations across wireless and computing layers with improved representational efficiency. The offloading policy is optimized through a Proximal Policy Optimization (PPO) algorithm to jointly minimize task completion latency and energy consumption. Simulation results demonstrate that QGAT-DRL achieves faster convergence, lower latency, and superior energy efficiency compared with state-of-the-art baselines, demonstrating the promise of hybrid quantum–classical learning for scalable resource orchestration in next-generation edge–cloud networks.