Quantum Algorithms applied to Satellite Mission Planning for Earth Observation

Earth imaging satellites are a crucial part of our everyday lives that enable global tracking industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there limitations; difficult manufacture, expensive maintain, tricky launch into orbit. Therefore, must be employed efficiently. This poses challenge known as the satellite mission planning problem, which could computationally prohibitive solve on large scales. close-to-optimal algorithms, such greedy reinforcement learning optimization can often provide satisfactory resolutions. paper introduces set quantum algorithms problem demonstrate an advantage over classical implemented thus far. The is formulated maximizing number high-priority tasks completed real datasets containing thousands multiple satellites. work demonstrates through solution-chaining clustering, machine offer greatest potential for optimal solutions. notably illustrates hybridized quantum-enhanced agent achieve completion percentage 98.5% tasks, significantly improving baseline methods with rate 75.8%. results presented in this pave way quantum-enabled solutions space industry and, more generally, future problems across industries.