Space Debris Removal Optimization Using Quantum Annealing

The exponential growth of space debris in Low Earth Orbit (LEO) poses a significant challenge to the sustainability of space operations. Despite preventive measures aimed at limiting debris generation, they remain insufficient to address the increasing accumulation of defunct satellites, rocket stages, and collision fragments. Active Debris Removal (ADR) has emerged as a promising solution, particularly in multi-target missions, which require solving complex combinatorial optimization problems similar to the Traveling Salesman Problem (TSP) to maximize the efficiency of the missions, minimizing fuel use and mission duration. This thesis explores the application of Quantum Annealing (QA) and Hybrid Quantum Annealing (HQA) to optimize multi-target ADR missions. Specifically, it introduces a Quadratic Unconstrained Binary Optimization (QUBO) model tailored for ADR using quantum computing frameworks to enhance solution efficiency. The research develops a generalized quadratization method to reduce computational complexity, enabling large-scale mission planning. Additionally, it proposes a novel constraint-handling strategy, embedding mission constraints in post-processing to improve quantum solver performance. The proposed approach is applied to real-world satellite debris datasets and benchmarked against classical metaheuristic optimizers, including Simulated Annealing (SA), Tabu Search (TS), and Genetic Algorithms (GA). The results highlight the potential of quantum optimization for ADR mission planning, offering a scalable and computationally efficient solution. This research represents one of the first applications of quantum computing to orbital debris management, contributing to the advancement of sustainable space operations.