In this paper, we aim to address the challenge of hybrid mobile edge-quantum computing (MEQC) for sustainable task offloading scheduling in mobile networks. We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks. Specifically, this is a time-coupled offloading scheduling optimization problem in need of a computationally affordable and effective solution. To this end, we propose a deep reinforcement learning (DRL)-based Lyapunov approach. More precisely, we reformulate the original time-coupled challenge into a mixed-integer optimization problem by introducing a penalty part in terms of virtual queues constructed by time-coupled constraints to the objective function. Subsequently, a Deep Q-Network (DQN) is adopted for task offloading mode selection. In addition, we design the Deep Deterministic Policy Gradient (DDPG)-based algorithm for partial-task offloading decision-making. Finally, tested in a realistic network setting, extensive experiment results demonstrate that our proposed approach is significantly more cost-effective and sustainable compared to existing methods.

翻译：本文旨在解决移动网络中面向可持续任务卸载调度的混合移动边缘-量子计算（MEQC）挑战。我们针对任务卸载模式选择与资源分配，开发了成本高效的设计方案，在满足移动设备个体链路延迟约束保证的同时，确保其计算任务所需成功率。具体而言，这是一个时间耦合的卸载调度优化问题，需要兼顾计算可行性与有效性的解决方案。为此，我们提出了一种基于深度强化学习（DRL）的Lyapunov方法。更精确地，我们通过引入由时间耦合约束构造的虚拟队列惩罚项到目标函数中，将原始时间耦合挑战重构为一个混合整数优化问题。随后，采用深度Q网络（DQN）进行任务卸载模式选择。此外，我们设计了基于深度确定性策略梯度（DDPG）的算法用于部分任务卸载决策。最后，在真实网络环境下进行测试，大量实验结果表明，与现有方法相比，我们提出的方法在成本效益与可持续性方面具有显著优势。