基于贝叶斯优化的量子网络样本有效自适应标定
Sample-efficient adaptive calibration of quantum networks using Bayesian optimization
摘要：
不可分辨的光子对于先进的量子通信网络是必不可少的。由于环境引起的光子变换和通信信道赋予的损失，特别是在嘈杂的情况下，难以获得不可分辨性。缓解这些转换的策略通常需要硬件或软件开销，这对于部署的网络来说是限制性的（例如添加噪声）、不可行的（例如在卫星上）或耗时的。在这里，我们提出并开发了资源高效的贝叶斯优化技术，以快速和自适应地校准量子网络中单个光子的不可分辨性，只使用从测量中获得的信息。为了实验验证我们的方法，我们展示了两个光子之间的Hong-Ou-Mandel干涉的优化-量子网络的中心任务-在存在高损耗和散粒噪声的情况下，找到快速，高效和可靠的收敛到最大光子不可逆性。我们希望我们的资源优化和实验友好的方法将允许快速和可靠的校准不可区分的量子，在分布式量子计算，通信和传感，以及基础研究的必要任务。
英文摘要：
Indistinguishable photons are imperative for advanced quantum communication networks. Indistinguishability is difficult to obtain because of environment-induced photon transformations and loss imparted by communication channels, especially in noisy scenarios. Strategies to mitigate these transformations often require hardware or software overhead that is restrictive (e.g. adding noise), infeasible (e.g. on a satellite), or time-consuming for deployed networks. Here we propose and develop resource-efficient Bayesian optimization techniques to rapidly and adaptively calibrate the indistinguishability of individual photons for quantum networks using only information derived from their measurement. To experimentally validate our approach, we demonstrate the optimization of Hong-Ou-Mandel interference between two photons -- a central task in quantum networking -- finding rapid, efficient, and reliable convergence towards maximal photon indistinguishability in the presence of high loss and shot noise. We expect our resource-optimized and experimentally friendly methodology will allow fast and reliable calibration of indistinguishable quanta, a necessary task in distributed quantum computing, communications, and sensing, as well as for fundamental investigations.
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