Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are also limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, it is critical that satellites are employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms can often provide satisfactory resolutions, such as greedy reinforcement learning, and optimization algorithms. This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. Most notably, this paper illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, which is a significant improvement over the baseline greedy methods with a completion rate of 63.6%. The results presented in this work pave the way to quantum-enabled solutions in the space industry and, more generally, future mission planning problems across industries.

翻译：地球成像卫星是我们日常生活中至关重要的组成部分，能够实现对全球工业活动的追踪。其应用场景涵盖天气预报、数字地图、碳足迹追踪以及植被监测等多个领域。然而，卫星也存在局限性：制造难度大、维护成本高、发射入轨过程复杂。因此，如何高效利用卫星成为关键问题。这引出了卫星任务规划问题，该问题在大规模求解时可能在计算上代价高昂。然而，接近最优的算法往往能提供令人满意的解决方案，例如贪婪强化学习算法与优化算法。本文提出了一组量子算法来解决任务规划问题，并展示了其相较于现有经典算法的优势。该问题被建模为：在包含数千个任务和多颗卫星的真实数据集上，最大化高优先级任务的完成数量。研究表明，通过解决方案链式聚合与聚类，优化算法与机器学习算法在实现最优解方面展现出最大潜力。尤为重要的是，本文证明了一种混合量子增强强化学习智能体能够完成98.5%的高优先级任务，相较于基线贪婪方法63.6%的完成率有显著提升。本文呈现的成果为航天工业中的量子赋能解决方案奠定了基础，并更广泛地推动了各行业未来任务规划问题的研究。