Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

QUARGS - Quantum Reinforced Ground Station Scheduling

Daniel Alexander Leidreiter, Andreas Petrak, Michael Alexander Anderle, Sven Prüfer

Published Mar 24, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.82217/spaceops2025_267

Related papers

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

Nils Quetschlich, Vincent Koch, Lukas Burgholzer +1 more

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 avail-able 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.

arXiv:2308.00029•IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•11 citations

Scalable Scheduling Policies for Quantum Satellite Networks

Albert Williams, Nitish K. Panigrahy, Andrew McGregor +1 more

As Low Earth Orbit (LEO) satellite mega constellations continue to be deployed for satellite internet and recent successful experiments in satellite-based quantum entanglement distribution emerge, a natural question arises: How should we coordinate transmissions and design scalable scheduling policies for a quantum satellite internet? In this work, we consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We then propose four heuristic algorithms and evaluate their performance for Starlink mega constellation under various amount of resources and placements of the ground stations. We find that the maximum number of receivers necessary per ground station grows very slowly with the total number of deployed ground stations. Our proposed algorithms, leveraging optimal weighted b-matching and the global greedy heuristic, outperform others in entanglement distribution rate, entanglement fidelity, and handover cost metrics. While we develop these scheduling algorithms, we have also designed a software system to simulate, visualize, and evaluate satellite mega-constellations for entanglement distribution.

arXiv:2405.09464•IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•6 citations

Quantum Optimization Methods for Satellite Mission Planning

Antón Makarov, Carlos Pérez-Herradón, Giacomo Franceschetto +5 more

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.

arXiv:2404.05516•IEEE Access•Jan 1, 2024•18 citations

Quantum algorithms for drone mission planning

Ethan G. Davies, Pranav V. Kalidindi

Mission planning often involves optimising the use of ISR (Intelligence, Surveillance and Reconnaissance) assets in order to achieve a set of mission objectives within allowed parameters subject to constraints. The missions of interest here, involve routing multiple UAVs visiting multiple targets, utilising sensors to capture data relating to each target. Finding such solutions is often an NP-Hard problem and cannot be solved efficiently on classical computers. Furthermore, during the mission new constraints and objectives may arise, requiring a new solution to be computed within a short time period. To achieve this we investigate near term quantum algorithms that have the potential to offer speed-ups against current classical methods. We demonstrate how a large family of these problems can be formulated as a Mixed Integer Linear Program (MILP) and then converted to a Quadratic Unconstrained Binary Optimisation (QUBO). The formulation provided is versatile and can be adapted for many different constraints with clear qubit scaling provided. We discuss the results of solving the QUBO formulation using commercial quantum annealers and compare the solutions to current edge classical solvers. We also analyse the results from solving the QUBO using Quantum Approximate Optimisation Algorithms (QAOA) and discuss their results. Finally, we also provide efficient methods to encode to the problem into the Variational Quantum Eigensolver (VQE) formalism, where we have tailored the ansatz to the problem making efficient use of the qubits available.

arXiv:2409.18631•SPIE Quantum Technologies for Defence and Security•Nov 15, 2024•3 citations

Optimal Entanglement Distribution using Satellite Based Quantum Networks

Nitish K. Panigrahy, Prajit Dhara, Don Towsley +2 more

Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.

arXiv:2205.12354•IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)•May 2, 2022•27 citations

Scheduling in Quantum Satellite Networks: Fairness and Performance Optimization

Ashutosh Jayant Dikshit, Naga Lakshmi Anipeddi, Prajit Dhara +5 more

Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.

quant-pharXiv:2512.07108•Dec 9, 2025•0 citations
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.
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 avail-able 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.
As Low Earth Orbit (LEO) satellite mega constellations continue to be deployed for satellite internet and recent successful experiments in satellite-based quantum entanglement distribution emerge, a natural question arises: How should we coordinate transmissions and design scalable scheduling policies for a quantum satellite internet? In this work, we consider the problem of transmission scheduling in quantum satellite networks subject to resource constraints at the satellites and ground stations. We show that the most general problem of assigning satellites to ground station pairs for entanglement distribution is NP-hard. We then propose four heuristic algorithms and evaluate their performance for Starlink mega constellation under various amount of resources and placements of the ground stations. We find that the maximum number of receivers necessary per ground station grows very slowly with the total number of deployed ground stations. Our proposed algorithms, leveraging optimal weighted b-matching and the global greedy heuristic, outperform others in entanglement distribution rate, entanglement fidelity, and handover cost metrics. While we develop these scheduling algorithms, we have also designed a software system to simulate, visualize, and evaluate satellite mega-constellations for entanglement distribution.
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.
Mission planning often involves optimising the use of ISR (Intelligence, Surveillance and Reconnaissance) assets in order to achieve a set of mission objectives within allowed parameters subject to constraints. The missions of interest here, involve routing multiple UAVs visiting multiple targets, utilising sensors to capture data relating to each target. Finding such solutions is often an NP-Hard problem and cannot be solved efficiently on classical computers. Furthermore, during the mission new constraints and objectives may arise, requiring a new solution to be computed within a short time period. To achieve this we investigate near term quantum algorithms that have the potential to offer speed-ups against current classical methods. We demonstrate how a large family of these problems can be formulated as a Mixed Integer Linear Program (MILP) and then converted to a Quadratic Unconstrained Binary Optimisation (QUBO). The formulation provided is versatile and can be adapted for many different constraints with clear qubit scaling provided. We discuss the results of solving the QUBO formulation using commercial quantum annealers and compare the solutions to current edge classical solvers. We also analyse the results from solving the QUBO using Quantum Approximate Optimisation Algorithms (QAOA) and discuss their results. Finally, we also provide efficient methods to encode to the problem into the Variational Quantum Eigensolver (VQE) formalism, where we have tailored the ansatz to the problem making efficient use of the qubits available.
Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.
Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.