# Probing many-body localization with neural networks

**Authors:** Frank Schindler, Nicolas Regnault, Titus Neupert

arXiv: 1704.01578 · 2017-07-04

## TL;DR

This paper demonstrates that neural networks trained on entanglement spectra can effectively identify the many-body localization transition in quantum systems, outperforming traditional methods and providing detailed eigenstate structure insights.

## Contribution

The study introduces a neural network approach to classify many-body localization transitions using entanglement spectra, achieving high accuracy and spatial resolution.

## Key findings

- Neural network accurately maps the phase diagram of the transition.
- Outperforms conventional methods in classifying eigenstates.
- Learns the power-law structure of entanglement spectra in localized regimes.

## Abstract

We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/1704.01578/full.md

## References

54 references — full list in the complete paper: https://tomesphere.com/paper/1704.01578/full.md

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Source: https://tomesphere.com/paper/1704.01578