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 limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, satellites must be 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, such as greedy reinforcement learning and optimization algorithms, can often provide satisfactory resolutions. 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. This paper notably illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, significantly improving over the baseline greedy methods with a completion rate of 75.8%. 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不可或缺的一部分，能够实现全球工业活动的跟踪。其应用场景涵盖从天气预报到数字地图、碳足迹追踪以及植被监测等众多领域。然而，卫星存在制造困难、维护成本高昂以及发射入轨复杂等局限性。因此，必须高效地利用卫星。这带来了被称为卫星任务规划问题的挑战，该问题在大规模求解时可能在计算上不可行。不过，接近最优的算法，如贪婪强化学习和优化算法，通常能提供令人满意的解决方案。本文介绍了一组用于解决任务规划问题的量子算法，并论证了其相较于当前已实现经典算法的优势。该问题被表述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。本研究表明，通过解链和聚类，优化与机器学习算法在获取最优解方面展现出最大潜力。本文特别指出，一种混合量子增强强化学习智能体在高优先级任务上可实现98.5%的完成率，显著优于完成率为75.8%的基准贪婪方法。本文所呈现的结果为航天工业中的量子赋能解决方案铺平了道路，并更广泛地适用于未来各行业的任务规划问题。