Quantum Algorithms applied to Satellite Mission Planning for Earth Observation

This paper introduces quantum algorithms for solving the satellite mission planning problem, demonstrating advantages over existing classical methods. The problem is formulated as maximizing high-priority task completion using real datasets. A hybridized quantum-enhanced reinforcement learning agent achieves a 98.5% completion rate for high-priority tasks, significantly outperforming a greedy baseline (75.8%). The results suggest promising quantum-enabled solutions for the space industry and other mission planning applications.

Serge Rainjonneau, Igor Tokarev, Sergei Iudin, Saaketh Rayaprolu, Karan Pinto, Daria Lemtiuzhnikova, Miras Koblan, Egor Barashov, Mohammad Kordzanganeh, Markus Pflitsch, Alexey Melnikov