Abstract
Building larger and more complex imaging satellite constellations challenges managing multiple Earth surface acquisition requests; reverse quantum annealing improves planning by leveraging hybrid quantum-classical methods.
Abstract
The trend of building larger and more complex imaging satellite constellations leads to the challenge of managing multiple acquisition requests of the Earth's surface. Optimally planning these acquisitions is an intractable optimization problem, and heuristic algorithms are used today for finding sub-optimal solutions. Recently, quantum algorithms have been considered for this purpose due to the potential breakthroughs they can bring in optimization, expecting either a speedup or an increase in solution quality. Hybrid quantum-classical methods have been considered a short-term solution for taking advantage of small quantum machines. In this paper, we propose reverse quantum annealing as a method for improving the acquisition plan obtained by a classical optimizer. We investigate the benefits of the method with different annealing schedules and different problem sizes. The obtained results provide guidelines on designing a larger hybrid quantum-classical framework based on reverse quantum annealing for this application.
