With the rapid advancement of artificial intelligence (AI) and intelligent science, intelligent edge computing has been widely adopted. However, the limitations of traditional methods, such as poor adaptability and the slow convergence of heuristic algorithms, are becoming increasingly evident. To enable sustainable and resource-efficient edge applications, this paper proposes an online task offloading framework for wireless powered mobile edge computing (MEC) networks, called Quantum Attention-based Reinforcement learning for Online Offloading (QAROO). The system employs a binary offloading strategy with the aim of co-optimizing computing and energy resources in dynamic channel environments. In response to the issues of poor adaptability in traditional approaches and the slow convergence of heuristic algorithms, the framework integrates quantum neural networks and attention mechanisms, introducing three key improvements: using recurrent neural networks to enhance temporal modeling capability, proposing an uncertainty-guided quantization method to improve exploration efficiency, and incorporating attention mechanisms into quantum networks to strengthen feature representation. Experiments demonstrate that the proposed method outperforms comparative schemes in terms of normalized computation speed and processing time, offering an efficient and stable solution for online task offloading in large-scale Internet of Things (IoT) dynamic environments.

翻译：随着人工智能（AI）与智能科学的快速发展，智能边缘计算已得到广泛应用。然而，传统方法（如自适应能力差与启发式算法收敛缓慢等）的局限性日益凸显。为实现可持续且资源高效的边缘应用，本文提出一种面向无线供能移动边缘计算（MEC）网络的在线任务卸载框架——基于量子注意力的在线卸载强化学习（QAROO）。该系统采用二元卸载策略，旨在动态信道环境下协同优化计算与能量资源。针对传统方法自适应能力差与启发式算法收敛缓慢的问题，该框架融合量子神经网络与注意力机制，引入三项关键改进：利用循环神经网络增强时序建模能力；提出不确定性引导的量化方法提升探索效率；在量子网络中嵌入注意力机制强化特征表征。实验结果表明，所提方法在归一化计算速度与处理时间上均优于对比方案，为大规模物联网（IoT）动态环境下的在线任务卸载提供了高效稳定的解决方案。