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.

翻译：由于地面量子链路存在根本性局限性，卫星作为全球量子互联网中纠缠态生成源的潜力已受到广泛关注。本研究聚焦于为此类量子网络设计卫星星座的问题。我们通过优化卫星倾角与卫星集群分配方案，实现了面向全球固定分布地面站的最大纠缠态生成速率。通过探索两种黑盒优化框架——贝叶斯优化方法与遗传算法方法，我们获得了具有可比性的优化结果，表明这两种方法对此类优化任务均具有效性。虽然遗传算法与贝叶斯优化通常表现极为相似，但贝叶斯优化往往收敛效率更高，而遗传算法后期表现出的增长趋势则表明其对局部最优解的敏感性较低。两种方法相较于仅针对地面站布局最大化覆盖率的朴素方法，均实现了显著性能提升。