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Quantum Machine Learning

Interpretable and unsupervised phase classification

arXiv

Authors: Julian Arnold, Frank Schäfer, Martin Žonda, Axel U. J. Lode

Year

2020

Paper ID

20069

Status

Preprint

Abstract Read

~2 min

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Abstract

Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method for phase classification which is rendered interpretable via an analytical derivation of its optimal predictions and allows for an automated construction scheme for order parameters. Based on these findings, we propose and apply an alternative, physically-motivated, data-driven scheme which relies on the difference between mean input features. This mean-based method is computationally cheap and directly interpretable. As an example, we consider the physically rich ground-state phase diagram of the spinless Falicov-Kimball model.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2010.04730 [2] arXiv https://arxiv.org/abs/2010.04730 [3] Publisher https://arxiv.org/abs/2010.04730

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