This paper exploits the potential of edge intelligence empowered satellite-terrestrial networks, where users' computation tasks are offloaded to the satellites or terrestrial base stations. The computation task offloading in such networks involves the edge cloud selection and bandwidth allocations for the access and backhaul links, which aims to minimize the energy consumption under the delay and satellites' energy constraints. To address it, an alternating direction method of multipliers (ADMM)-inspired algorithm is proposed to decompose the joint optimization problem into small-scale subproblems. Moreover, we develop a hybrid quantum double deep Q-learning (DDQN) approach to optimize the edge cloud selection. This novel deep reinforcement learning architecture enables that classical and quantum neural networks process information in parallel. Simulation results confirm the efficiency of the proposed algorithm, and indicate that duality gap is tiny and a larger reward can be generated from a few data points compared to the classical DDQN.

翻译：本文探讨了边缘智能赋能的星地网络潜力，其中用户的计算任务被卸载至卫星或地面基站。此类网络中的计算任务卸载涉及边缘云选择以及接入与回程链路的带宽分配，其目标是在延迟和卫星能量约束下最小化能耗。为解决该问题，本文提出一种受交替方向乘子法（ADMM）启发的算法，将联合优化问题分解为小规模子问题。此外，我们开发了一种混合量子双深度Q学习（DDQN）方法来优化边缘云选择。这种新颖的深度强化学习架构使得经典神经网络与量子神经网络能够并行处理信息。仿真结果验证了所提算法的有效性，表明其对偶间隙极小，且相较于经典DDQN，仅需少量数据点即可获得更高的奖励。