标题： 机器学习，量子混沌，伪随机演化 显示英文标题

标题： Machine learning, quantum chaos, and pseudorandom evolution

摘要： 通过用随机算子集合对量子混沌动力学进行建模，我们探讨了机器学习算法如何用于检测量子比特系统中的伪随机行为。我们分析了由关联函数片段组成的样本，并发现机器学习算法能够在精确的意义上确定系统所经历的伪随机程度。 这是在不显式计算任何关联器的情况下完成的。 有趣的是，甚至从两点函数中抽取的样本也被发现足以解决这个分类问题。 这为使用深度学习算法探索以前无法通过模拟访问的混沌量子系统的后期时间行为提供了可能性。 显示英文摘要

摘要： By modeling quantum chaotic dynamics with ensembles of random operators, we explore howmachine learning learning algorithms can be used to detect pseudorandom behavior in qubit systems.We analyze samples consisting of pieces of correlation functions and find that machine learningalgorithms are capable of determining the degree of pseudorandomness which a system is subjectto in a precise sense. This is done without computing any correlators explicitly. Interestingly,even samples drawn from two-point functions are found to be sufficient to solve this classificationproblem. This presents the possibility of using deep learning algorithms to explore late time behaviorin chaotic quantum systems which have been inaccessible to simulation.