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Communications in computer and information science

Scheduling of Satellite Constellation Operations in EO Missions Using Quantum Optimization

Vinicius Marchioli, Mattia Boggio, Deborah Volpe, Luca Massotti, Carlo Novara

Published Jan 1, 2024• 8 citations
Publisher
DOI
10.1007/978-3-031-77432-4_16

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Consider a constellation of n satellites in medium-earth orbit as well as k ground stations distributed over the Earth. Such satellites require semiregular contacts with mission control in order to upload telecommands or general purpose maintenances, which is done via ground stations. In order to plan ground station contacts for such a constellation it is necessary to calculate possible visibilities between satellite--ground station pairs, and then build a valid schedule from that data which satisfies all mission-specific constraints. This is a typical challenge for the mission planning system of a satellite constellation. In addition, there is often some kind of relevant optimization goal, for example fairness conditions or minimization of total number of contacts to have more time available for the actual missions objectives. Depending on the constraints for such ground station contact plans as well as the size of n and k, this problem may be hard to solve optimally. In particular, if k is much smaller than n it may be non-trivial to find any solution at all. For such problems one often employs global optimizers such as Gurobi, CPLEx or SCIP, or problem-specific heuristic search algorithms that try to find a solution that is good enough for practical purposes. While such approaches can be made to work for most real scenarios, classical solvers tend to struggle to find solutions in a reasonable time for very large satellite constellations. Due to this reason, we investigate potential applications of quantum computers to larger instances of this problem in this paper. To this end, we developed a library called QUARGS (Quantum Reinforced Ground Station Scheduling) which allows solving examples for such ground station scheduling problems using different quantum algorithms. In this paper we compare and evaluate these different solutions against each other as well as against a classical solution using the global optimizer SCIP. After an overview of similar problems in the literature we define the concrete problem including all constraints as well as possible optimization functions. Then we give a brief overview of quantum algorithms that may be used to solve such scheduling problems. This includes in particular quantum annealing (QA), quantum approximate optimization algorithm (QAOA), variational quantum eigensolvers (VQE), as well as evolutionary variational quantum eigensolvers (E-VQE). The latter in particular was implemented and published in an open-source Python library QUEASARS (Quantum Evolving Ansatz Variational Solver). Besides evaluating these algorithms on sample cases regarding their solution quality and quantum circuit depths, we discuss difficulties for the implementation of various constraints in the algorithms and their potential solutions.
Abstract With the goal of a space-based quantum network is to have satellites distribute keys between any nodes on the ground, we consider an evolved quantum network from a near-term form, in which a space-based relay, Micius, executes a sequence of Satellite QKD (SatQKD) missions, allowing any two cities to have a shared key. Accordingly, we develop a comprehensive framework integrated with precise orbital modelling and a cloud statistics model to enable a preassessment of SatQKD. Using this framework, we consider three different scheduling strategies and estimate the keys that can be delivered to cities. By the assistance of using different optimizations on scheduling problem formulations, it is possible to allows for the possibility to consider strategies for different missions such as extending connection for distant nodes, prioritized delivery to nodes with higher privileges, and promoting keys utilization. We also provide a comparison of the total number of keys delivered using different-altitude satellites. It is demonstrated that the plan for constructing a low-Earth orbit (LEO) satellite constellation is more efficient than that for employing an expensive high-orbit satellite to an execution of scheduling SatQKD.
The trend of building larger and more complex imaging satellite constellations leads to the challenge in managing multiple acquisition requests of the Earth 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 that they can bring in optimization, expecting either a speedup or an increase in the solution quality. Hybrid quantum-classical methods have been considered as 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.