Hybrid Quantum–Classical Optimization for Satellite Constellation Design and Orbital Debris Management

Abstract

The rapid expansion of satellite constellations and the growing density of orbital debris have intensified the need for advanced computational tools capable of optimizing orbital architectures, collision avoidance, and long-term space sustainability. Traditional optimization methods are increasingly constrained by the combinatorial complexity of constellation design variables—including orbital planes, phasing, revisit requirements, and communication coverage—and the nonlinear dynamics of debris propagation. This study introduces a hybrid quantum–classical optimization framework that integrates quantum approximate optimization algorithms (QAOA), quantum annealing, and classical multi-physics orbital simulation to improve decision-making in constellation deployment and debris mitigation. Quantum subroutines accelerate the search for globally efficient orbital configurations, while classical solvers handle high-fidelity astrodynamics, propagation models, and mission constraints. Preliminary simulation results indicate that the hybrid framework yields more efficient constellation geometries, reduces collision risk, and enhances debris avoidance planning compared to classical baselines. These findings highlight the potential of quantum-assisted optimization to support safer, more resilient, and sustainably managed space systems.