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arXiv 1809.03124quant-phcond-mat.quant-gas
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用机器学习逼近绝热时标

Approaching the adiabatic timescale with machine-learning

Bryce M. Henson, Dong K. Shin, Kieran F. Thomas, Jacob A. Ross, Michael R. Hush, Sean S. Hodgman, Andrew G. Truscott

备注：
7 pages main text, 2 pages supporting information
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摘要：

没有激发的量子系统的控制和操纵是具有挑战性的，这是由于精确地完全建模这种系统的复杂性以及在实验上控制这些固有脆弱系统的困难。例如，虽然理论上已经很好地开发了使玻色-爱因斯坦凝聚体（BEC）比绝热时间尺度（没有激发或损失）更快的方案，但是这些方案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，我们通过实验证明了一种基于机器学习算法的替代方法，该方法朝着这一目标取得了进展。该算法给出了控制的耦合减压和运输的亚稳态氦冷凝物，其性能确定后，通过测量所得到的BEC的激发每次实验迭代。在每次迭代之后，算法调整其系统的内部模型以创建用于下一次迭代的改进的控制输出。在充分控制解压缩的情况下，该算法收敛到一个新的解决方案，该解决方案设置了与绝热时间尺度相关的当前速度记录，击败了基于理论方法的其他实验实现。这种方法为在其他量子系统中实现快速态制备或变换提供了一种可行的方法，而不需要对系统的理论模型进行求解。基础物理和冷却的影响进行了讨论。

英文摘要：

The control and manipulation of quantum systems without excitation is 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 (BEC) 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 towards 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 novel 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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