Automating Antenna Scheduling Problems Using Quantum Computing and Deep Reinforcement Learning

A challenging, week-long antenna scheduling problem (12 antennas, 30 missions to track) was solved using three techniques: formulated as a QUBO problem and solved on the D-Wave hybrid (quantum-classical) solver, formulated as a MILP problem and conventionally solved, and formulated as a DeepRL problem in which an agent learns a policy to deconflict the schedule. All three techniques were able to solve overscheduled problems for multiple weeks with competitive results for the satisfied time fraction for each mission (actual / requested). The quantum hybrid approach showed promise for scaling to larger problems with shorter running times.