Published Paper Details:

The proliferation of space debris poses an existential threat to the sustainability of operations in Low Earth Orbit (LEO). While classical Artificial Intelligence (AI) solutions have improved tracking and localized debris capture, they encounter significant computational intractability when planning large-scale, multi-target Active Debris Removal (ADR) missions. This paper proposes a Hybrid Quantum-Classical (HQC) framework specifically designed to overcome these combinatorial optimization bottlenecks. The framework leverages Quantum Annealing (QA) to efficiently solve the optimal routing problem (ORP), formulated as a high-fidelity Quadratic Unconstrained Binary Optimization (QUBO) model. This optimization is integrated with Quantum Machine Learning (QML) for accelerated Space Situational Awareness (SSA) and real-time collision risk assessment (Pc). Simulation results benchmarking the HQC optimizer against classical metaheuristics, such as Genetic Algorithms (GA) and Simulated Annealing (SA), demonstrate a superior solution quality (98% near-optimal fuel consumption) and a substantial reduction in time-to-solution (a 10-fold speedup for N=50 targets). Furthermore, the application of Variational Quantum Algorithms (VQAs) for quantum-enhanced anomaly detection improves sensor data fidelity and strengthens autonomous decision-making robustness, validating the critical role of nascent quantum technologies in preserving the orbital environment against the escalating threat of Kessler Syndrome.