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Approaching the adiabatic timescale with machine learning [Physics]
Proceedings of the National Academy of Sciences of the United States of America ( IF 9.4 ) Pub Date : 2018-12-26 , DOI: 10.1073/pnas.1811501115
Bryce M. Henson 1 , Dong K. Shin 1 , Kieran F. Thomas 2 , Jacob A. Ross 1 , Michael R. Hush 3 , Sean S. Hodgman 1 , Andrew G. Truscott 1
Affiliation  

The control and manipulation of quantum systems without excitation are challenging, due to the complexities in fully modeling such systems accurately and the difficulties in controlling these inherently fragile systems experimentally. For example, while protocols to decompress Bose–Einstein condensates (BECs) faster than the adiabatic timescale (without excitation or loss) have been well developed theoretically, experimental implementations of these protocols have yet to reach speeds faster than the adiabatic timescale. In this work, we experimentally demonstrate an alternative approach based on a machine-learning algorithm which makes progress toward this goal. The algorithm is given control of the coupled decompression and transport of a metastable helium condensate, with its performance determined after each experimental iteration by measuring the excitations of the resultant BEC. After each iteration the algorithm adjusts its internal model of the system to create an improved control output for the next iteration. Given sufficient control over the decompression, the algorithm converges to a solution that sets the current speed record in relation to the adiabatic timescale, beating out other experimental realizations based on theoretical approaches. This method presents a feasible approach for implementing fast-state preparations or transformations in other quantum systems, without requiring a solution to a theoretical model of the system. Implications for fundamental physics and cooling are discussed.



中文翻译:

通过机器学习逼近绝热时标[物理]

由于无法精确地对此类系统进行完全建模,并且难以通过实验控制这些固有的易碎系统,因此在没有激发的情况下进行量子系统的控制和操作具有挑战性。例如,尽管理论上已经很好地开发出了比绝热时间尺度(无激发或损失)更快地解压缩玻色-爱因斯坦冷凝物(BEC)的协议,但是这些协议的实验实现尚未达到比绝热时间尺度更快的速度。在这项工作中,我们实验性地演示了一种基于机器学习算法的替代方法,该方法正在朝这一目标迈进。该算法可控制亚稳态氦冷凝物的减压和输运耦合,在每次实验迭代后,通过测量所得BEC的激励来确定其性能。每次迭代后,算法都会调整其系统内部模型,以为下一次迭代创建改进的控制输出。在对减压进行充分控制的情况下,该算法收敛到一种设置方法,该方法设置与绝热时标有关的当前速度记录,从而超越了基于理论方法的其他实验实现。该方法提供了在其他量子系统中实现快速状态准备或转换的可行方法,而无需解决系统的理论模型。讨论了对基本物理和冷却的影响。每次迭代后,算法都会调整其系统内部模型,以为下一次迭代创建改进的控制输出。在对减压进行充分控制的情况下,该算法收敛到一种设置方法,该方法设置与绝热时标有关的当前速度记录,从而超越了基于理论方法的其他实验实现。该方法提供了在其他量子系统中实现快速状态准备或转换的可行方法,而无需解决系统的理论模型。讨论了对基本物理和冷却的影响。每次迭代后,算法都会调整其系统内部模型,以为下一次迭代创建改进的控制输出。在对减压进行充分控制的情况下,该算法收敛到一种设置方法,该方法设置与绝热时标有关的当前速度记录,从而超越了基于理论方法的其他实验实现。该方法提供了在其他量子系统中实现快速状态准备或转换的可行方法,而无需解决系统的理论模型。讨论了对基本物理和冷却的影响。击败了其他基于理论方法的实验实现。该方法提供了在其他量子系统中实现快速状态准备或转换的可行方法,而无需解决系统的理论模型。讨论了对基本物理和冷却的影响。击败了其他基于理论方法的实验实现。该方法提供了在其他量子系统中实现快速状态准备或转换的可行方法,而无需解决系统的理论模型。讨论了对基本物理和冷却的影响。

更新日期:2018-12-28
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