  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.
