Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …

We develop a machine learning method to construct accurate ground-state wave functions
of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A …

The Landau description of phase transitions relies on the identification of a local order
parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological …

Abstract Machine-learning techniques such as artificial neural networks are currently
revolutionizing many technological areas and have also proven successful in quantum …

In this paper, we demonstrate the expressibility of artificial neural networks (ANNs) in
quantum many-body physics by showing that a feed-forward neural network with a small …

In this Letter we supervisedly train neural networks to distinguish different topological
phases in the context of topological band insulators. After training with Hamiltonians of one …

Neural-network-based machine learning is emerging as a powerful tool for obtaining phase
diagrams when traditional regression schemes using local equilibrium order parameters are …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

A convolutional neural network (CNN) is designed to study correlation between the
temperature and the spin configuration of the two-dimensional Ising model. Our CNN is able …