为全球量子网络优化卫星轨道参数
Optimizing Orbital Parameters of Satellites for a Global Quantum Network
AI总结：
本文提出利用贝叶斯优化和遗传算法优化卫星轨道参数，以提高全球量子网络中纠缠生成的效率。
摘要：
由于地面量子链路存在根本限制，卫星因其作为纠缠生成源的潜力而受到广泛关注。在本工作中，我们聚焦于设计一个卫星星座以实现这一量子网络。我们发现卫星倾角和卫星集群分配，以实现最大纠缠生成率，以固定的一组全球分布地面站为目标。探索两种黑盒优化框架：贝叶斯优化（BO）方法和遗传算法（GA）方法，我们发现相似的结果，表明这些方法对于此优化任务的有效性。虽然GA和BO往往表现 remarkably 相似，但BO通常收敛更高效，而GA后期增长表明对局部极值的不敏感性。无论哪种情况，它们都比简单方法有显著改进，这些简单方法在地面站放置方面最大化覆盖。
英文摘要：
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.
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