Preparation of ordered states in ultra-cold gases using Bayesian optimization
Preparation of ordered states in ultra-cold gases using Bayesian optimization
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DOI：
10.1088/1367-2630/ab8677
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发表时间：
2020-01
影响因子：
3.3
通讯作者：
R. Mukherjee;F. Sauvage;Harry Xie;R. Loew;F. Mintert
中科院分区：
文献类型：
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作者：
R. Mukherjee;F. Sauvage;Harry Xie;R. Loew;F. Mintert
超冷原子气体在内部和外部自由度的可控程度方面都是独一无二的。这使得利用它们来研究复杂的量子多体现象成为可能。然而，在许多情况下，尽管存在退相干和系统缺陷，但忠实地准备所需量子态的先决条件并不总是得到充分满足。为了为达到特定的目标状态铺平道路，我们实现了基于贝叶斯优化的量子最优控制。贝叶斯优化的概率建模和广泛的探索方面特别适合数据采集成本高昂的量子实验。使用晶格中玻色子的超流体到莫特绝缘体转变的数值模拟以及里德堡晶体的形成作为明确的例子，我们证明与现有的最优控制方法相比，贝叶斯优化能够在有限和噪声数据方面找到更好的控制解决方案。
Ultra-cold atomic gases are unique in terms of the degree of controllability, both for internal and external degrees of freedom. This makes it possible to use them for the study of complex quantum many-body phenomena. However in many scenarios, the prerequisite condition of faithfully preparing a desired quantum state despite decoherence and system imperfections is not always adequately met. To pave the way to a specific target state, we implement quantum optimal control based on Bayesian optimization. The probabilistic modeling and broad exploration aspects of Bayesian optimization are particularly suitable for quantum experiments where data acquisition can be expensive. Using numerical simulations for the superfluid to Mott-insulator transition for bosons in a lattice as well as for the formation of Rydberg crystals as explicit examples, we demonstrate that Bayesian optimization is capable of finding better control solutions with regards to finite and noisy data compared to existing methods of optimal control.