Quantum Optimization Methods for Satellite Mission Planning

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 underscor...

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Vydáno v:IEEE access Ročník 12; s. 71808 - 71820
Hlavní autoři: Makarov, Anton, Perez-Herradon, Carlos, Franceschetto, Giacomo, Taddei, Marcio M., Osaba, Eneko, del Barrio Cabello, Paloma, Villar-Rodriguez, Esther, Oregi, Izaskun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Annealing
Approximation algorithms
Cameras
Combinatorial analysis
Combinatorial optimization
earth observation
Fields (mathematics)
Graph theory
Mission planning
Optimization
Optimization methods
Planning
Quantum annealing
quantum approximate optimization algorithm
Quantum computers
Quantum computing
satellite mission planning
Satellite observation
Satellites
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3402990