https://doi.org/10.1140/epjqt/s40507-025-00409-3
Research
Quantum optimization for multi-target Active Debris Removal missions
1
Politecnico di Torino, Corso Duca degli Abruzzi 24, 10124, Turin, Italy
2
Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
a
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Received:
18
March
2025
Accepted:
13
August
2025
Published online:
29
September
2025
The rapid accumulation of space debris in Low Earth Orbit (LEO) poses a significant challenge to the sustainability of space operations. While preventive measures limit new debris generation, they are insufficient to mitigate the growing population of defunct satellites, rocket stages, and collision fragments. Active Debris Removal (ADR) has emerged as a viable solution, which requires solving NP-hard combinatorial optimization problem similar to the Traveling Salesman Problem (TSP) to maximize mission efficiency by minimizing fuel and mission duration. This work explores the application of Quantum Annealing (QA) and Hybrid Quantum Annealing (HQA) for optimizing multi-target ADR missions. Specifically, it introduces a Quadratic Unconstrained Binary Optimization (QUBO) model for ADR, exploiting quantum computing to enhance solution efficiency. A novel quadratization method is developed to reduce computational complexity, enabling large-scale mission planning. Additionally, a novel constraint-handling strategy is proposed, integrating mission constraints into post-processing to enhance quantum solver efficiency. The proposed approach is validated using real-world satellite debris datasets and benchmarked against classical metaheuristic optimizers, including Simulated Annealing (SA), Tabu Search (TS), and Genetic Algorithms (GA). The obtained results demonstrate the advantages of quantum optimization for ADR mission planning, providing a scalable and computationally efficient solution.
Key words: QUBO / Quantum Annealer / Hybrid Quantum Annealing / Adiabatic Quantum Computing / Quantum Optimization / Debris Removal / Quantum Computing for Space / Space technology / Quadratization / Constraints Management
© The Author(s) 2025
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