A Hybrid Classical Quantum Computing Approach to the Satellite Mission Planning Problem

TL;DR

This paper introduces a hybrid classical-quantum approach using variational quantum algorithms to address the Satellite Mission Planning Problem, demonstrating potential improvements over classical methods for selecting imaging locations.

Contribution

It presents the first application of quantum algorithms like VQE, QAOA, and W-QAOA to SMPP, combining quantum hardware with classical optimization for satellite planning.

Findings

Successfully applied quantum algorithms to SMPP with up to 21 locations.

Demonstrated the feasibility of quantum approaches in satellite mission planning.

Provided a proof-of-concept implementation available on GitHub.

Abstract

Hundreds of satellites equipped with cameras orbit the Earth to capture images from locations for various purposes. Since the field of view of the cameras is usually very narrow, the optics have to be adjusted and rotated between single shots of different locations. This is even further complicated by the fixed speed -- determined by the satellite's altitude -- such that the decision what locations to select for imaging becomes even more complex. Therefore, classical algorithms for this Satellite Mission Planning Problem (SMPP) have already been proposed decades ago. However, corresponding classical solutions have only seen evolutionary enhancements since then. Quantum computing and its promises, on the other hand, provide the potential for revolutionary improvement. Therefore, in this work, we propose a hybrid classical quantum computing approach to solve the SMPP combining the advantages of quantum hardware with decades of classical optimizer development. Using the Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and its warm-start variant (W-QAOA), we demonstrate the applicability of solving the SMPP for up to 21 locations to choose from. This proof-of-concept -- which is available on GitHub (https://github.com/cda-tum/mqt-problemsolver) as part of the Munich Quantum Toolkit (MQT) -- showcases the potential of quantum computing in this application domain and represents a first step toward competing with classical algorithms in the future.