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Quantum Optimization Methods for Satellite Mission Planning
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DOI：10.1109/ACCESS.2024.3402990.png)
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Abstract
En 中文
Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today's quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities.
Keywords:
Satellites
Planning
Optimization methods
Cameras
Quantum annealing
Approximation algorithms
Annealing
Combinatorial optimization
earth observation
quantum annealing
quantum approximate optimization algorithm
quantum computing
satellite mission planning
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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