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arXiv 1902.07019quant-ph
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机器学习非马尔可夫量子动力学

Machine learning non-Markovian quantum dynamics

I. A. Luchnikov, S. V. Vintskevich, D. A. Grigoriev, S. N. Filippov

摘要:

机器学习方法已被证明对统计数据中的模式识别是有用的。在量子物理学中,测量结果本质上是随机的,然而,当在开放量子系统上连续进行测量时,它们确实具有模式。这种模式是由于系统与环境的相互作用,并包含有关弛豫率以及非马尔可夫记忆效应的信息。在这里,我们开发了一种方法来提取未知环境的信息,从一系列的投影单次测量系统(不诉诸过程层析成像)。该方法是基于嵌入的非马尔可夫系统的动力学的马尔可夫动力系统和有限维的有效水库。利用极大似然估计学习马尔可夫嵌入的生成元。我们通过比较它的预测与一个完全可解的非马尔可夫动力学验证的方法。所开发的学习未知量子环境的算法使人们能够有效地控制和操纵量子系统。

英文摘要:

Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successively on an open quantum system. This pattern is due to the system-environment interaction and contains information about the relaxation rates as well as non-Markovian memory effects. Here we develop a method to extract the information about the unknown environment from a series of projective single-shot measurements on the system (without resorting to the process tomography). The method is based on embedding the non-Markovian system dynamics into a Markovian dynamics of the system and the effective reservoir of finite dimension. The generator of Markovian embedding is learned by the maximum likelihood estimation. We verify the method by comparing its prediction with an exactly solvable non-Markovian dynamics. The developed algorithm to learn unknown quantum environments enables one to efficiently control and manipulate quantum systems.

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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