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A transition of quantum walk induced by classical randomness changes theprobability distribution of the walker from a two-peak structure to asingle-peak one when the random parameter exceeds a critical value. We firstestablish the generality of the localization by showing its emergence in thepresence of random rotation or translation. The transition point can be locatedmanually by examining the probability distribution, momentum of inertia, andinverse participation ratio. As a comparison, we implement three supervisedmachine learning methods, the support vector machine (SVM), multi-layerperceptron neural network, and convolutional neural network with the same dataand show they are able to identify the transition. While the SVM sometimesunderestimate the exponents compared to the manual methods, the twoneural-network methods show more deviation for the case with random translationdue to the fluctuating probability distributions. Our work illustratespotentials and challenges facing machine learning of physical systems withmixed quantum and classical probabilities.

Localization of quantum walk with classical randomness: Comparison between manual methods and supervised machine learning