Optimizing Orbital Parameters of Satellites for a Global Quantum Network
This work addresses the problem of designing a satellite constellation for a global quantum internet, which is significant for researchers and organizations seeking to establish a reliable and efficient quantum network.
The authors tackled the problem of optimizing satellite orbital parameters for a global quantum network, achieving substantial improvements over naive approaches with entanglement generation rates maximized through Bayesian Optimization and Genetic Algorithm methods. The results show comparable performance between the two methods, with Bayesian Optimization often converging more efficiently.
Due to fundamental limitations on terrestrial quantum links, satellites have received considerable attention for their potential as entanglement generation sources in a global quantum internet. In this work, we focus on the problem of designing a constellation of satellites for such a quantum network. We find satellite inclination angles and satellite cluster allocations to achieve maximal entanglement generation rates to fixed sets of globally distributed ground stations. Exploring two black-box optimization frameworks: a Bayesian Optimization (BO) approach and a Genetic Algorithm (GA) approach, we find comparable results, indicating their effectiveness for this optimization task. While GA and BO often perform remarkably similar, BO often converges more efficiently, while later growth noted in GAs is indicative of less susceptibility towards local maxima. In either case, they offer substantial improvements over naive approaches that maximize coverage with respect to ground station placement.