Scheduling of Agile Earth Observation Constellation using Quantum Annealing

As Earth Observation (EO) missions advance towards Agile Earth Observation Satellites, the complexity of scheduling problems increases, posing challenges for traditional optimization methods. This thesis investigates the potential of a quantum algorithm to address the scheduling problem in EO constellations. In particular, a novel formulation of the satellite constellation optimization problem is proposed, translating it into a Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., compliant with quantum solvers. Penalty functions are incorporated to optimize mission energy consumption. The formulated QUBO problem is then implemented and solved on a real quantum computer (a D-Wave Quantum Annealer). The performance provided by the quantum machine is compared with established classical meta-heuristic solvers like Simulated Annealing and Tabu Search. The results show that the proposed quantum optimization process achieves better results in terms of both solution quality and computational efficiency.