Earth observation satellites (EOS) play a crucial role in collecting data for applications like weather forecasting and disaster management. Optimizing EOS missions to capture high-priority targets within constraints such as storage, energy, and weather is challenging for traditional computing methods. Quantum computing shows promise in enhancing efficiency by exploring vast solution spaces for optimal schedules, even with complex constraints. This paper demonstrates the potential of a quantum algorithm to improve real-time EOS mission planning, maximizing high-priority target acquisition under resource limitations. Compared to classical algorithms like simulated annealing and Gurobi optimizer, the proposed quantum algorithm outperformed in selecting high-priority targets by 23.46%, executed faster than Gurobi by 39.09%, and successfully satisfied all constraints for all data.
Earth observation satellites (EOS) play a crucial role in collecting data for applications like weather forecasting and disaster management. Optimizing EOS missions to capture high-priority targets within constraints such as storage, energy, and weather is challenging for traditional computing methods. Quantum computing shows promise in enhancing efficiency by exploring vast solution spaces for optimal schedules, even with complex constraints. This paper demonstrates the potential of a quantum algorithm to improve real-time EOS mission planning, maximizing high-priority target acquisition under resource limitations. Compared to classical algorithms like simulated annealing and Gurobi optimizer, the proposed quantum algorithm outperformed in selecting high-priority targets by 23.46%, executed faster than Gurobi by 39.09%, and successfully satisfied all constraints for all data.
Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are limitations; satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, satellites must be employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms, such as greedy reinforcement learning and optimization algorithms, can often provide satisfactory resolutions. This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. This paper notably illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, significantly improving over the baseline greedy methods with a completion rate of 75.8%. The results presented in this work pave the way to quantum-enabled solutions in the space industry and, more generally, future mission planning problems across industries.
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.
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.
Abstract The scheduling complexity of Agile Earth Observation Satellites (AEOSs) increases significantly as Earth Observation missions progress. This makes traditional optimization techniques less effective, restricting their application to small-scale and open-loop scheduling problems. In this paper, we investigate the potential of quantum solvers to address the closed-loop scheduling problem for a formation of AEOSs, overcoming the limitations of classical optimization techniques. To this end, we formulate the scheduling problem as a novel Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., a formulation specifically designed for quantum solvers. Moreover, penalty functions are introduced to minimize mission energy consumption and reduce deviations between the original and rescheduled solutions. The formulated QUBO problem is implemented on a D-Wave quantum annealer for a daily and large-scale scheduling scenario. The obtained results demonstrate significant improvements in computational efficiency and solution quality compared to traditional methods like Simulated Annealing and Tabu Search, highlighting the potential of quantum solvers in optimizing complex scheduling tasks for AEOS formations.
Earth observation (EO) is increasingly driven by large-scale remote sensing (RS) data, acquired from satellite and airborne platforms across diverse temporal and spatial resolutions. These datasets are characterized not only by volume, but by complex properties such as multi-source heterogeneity, high dimensionality, nonlinear feature distributions, and spatio-temporal variability. Processing such data at operational scale introduces significant algorithmic and computational challenges, particularly in high resolution environmental monitoring and planetary-scale inference tasks. Quantum computing (QC) offers a computational paradigm fundamentally different from classical computing, leveraging the principles of quantum mechanics to perform operations in high-dimensional state spaces. This theoretical advantage makes QC a compelling candidate for selected EO tasks, especially those involving combinatorial optimization and learning tasks. However, the limited qubit fidelity and scale of current quantum hardware constrain their direct applicability to operational applications in Earth observation. This PhD thesis investigates the application of annealing-based and circuit-based quantum algorithms to EO, as well as the integration of quantum algorithms with classical devices, such as those in high-performance computing (HPC) environments. It examines acquisition scheduling and data classification tasks within EO workflows. Experiments assess practical algorithmic benefits, computational scalability, and constraints imposed by hybrid quantum-classical execution. Results demonstrate that quantum modules, when carefully embedded into HPC architectures, can enhance selected stages of EO pipelines, specifically using quantum machine learning and quantum optimization approaches.
The ongoing use of satellites is to get information about the earth's features by sensing the radiation reflected/emitted by various objects on the earth’s surface. Since the launch of the first satellite in the year 1957 for defence purposes only, satellites have now become an integral part of human life and play an important role in different sectors including communication, navigation, monitoring weather conditions, etc. These satellites are placed in the earth’s orbit continuously imaging the earth’s surface and generating data of huge sizes. The analysis of this large size data with the help of current generation classical computers is a time-consuming process resulting in a delay in observing the real-time phenomena happening on the earth's surface. The high processing capabilities of quantum computing have emerged as a promising solution for quick analysis of data received from the satellite which may help in monitoring hazards in real time. The quantum computer further will help improve the weather forecasting models and improve the capabilities of artificial neural networks to get useful information from large size data. Here, we will review the usage of quantum computation in existing satellite data processing, its applications in weather forecasting, and real-time monitoring of natural hazard situations through satellites.