Machine learning, quantum chaos, and pseudorandom evolution

TL;DR

This paper demonstrates that machine learning algorithms can effectively detect pseudorandom behavior in quantum chaotic systems using correlation function samples, enabling analysis of complex quantum dynamics without explicit correlator computation.

Contribution

It introduces a novel approach of using machine learning to classify pseudorandomness in quantum chaos, bypassing traditional computational methods.

Findings

ML algorithms can determine pseudorandomness degree from correlation samples

Two-point function samples suffice for classification

Deep learning enables exploration of late-time quantum chaos behavior

Abstract

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.