Magnetic nano-skyrmions develop quantized helicity excitations, and the quantum tunneling
between nano-skyrmions possessing distinct helicities is indicative of the quantum nature of …

Identifying phase transitions and classifying phases of matter is central to understanding the
properties and behavior of a broad range of material systems. In recent years, machine …

We apply modern methods in computational topology to the task of discovering and
characterizing phase transitions. As illustrations, we apply our method to four two …

Persistent homology (PH) is a relatively new field in applied mathematics that studies the
components and shapes of discrete data. In this paper, we demonstrate that PH can be used …

It is a hot topic how entanglement, a quantity from quantum information theory, can assist
machine learning. In this work, we implement numerical experiments to classify …

Neural networks (NNs) usually hinder any insight into the reasoning behind their
predictions. We demonstrate how influence functions can unravel the black box of NN when …

Recently, there has been an increased interest in the application of machine learning (ML)
techniques to a variety of problems in condensed-matter physics. In this regard, of particular …

We use a recently developed interpretable and unsupervised machine-learning method, the
tensorial kernel support vector machine, to investigate the low-temperature classical phase …

Kitaev materials are promising materials for hosting quantum spin liquids and investigating
the interplay of topological and symmetry-breaking phases. We use an unsupervised and …

Abstract Machine learning (ML) techniques applied to quantum many-body physics have
emerged as a new research field. While the numerical power of this approach is undeniable …