Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

We show how the success of deep learning could depend not only on mathematics but also
on physics: although well-known mathematical theorems guarantee that neural networks …

Machine learning, one of today's most rapidly growing interdisciplinary fields, promises an
unprecedented perspective for solving intricate quantum many-body problems …

State-of-the-art machine learning techniques promise to become a powerful tool in statistical
mechanics via their capacity to distinguish different phases of matter in an automated way …

Matrix product states (MPSs), a tensor network designed for one-dimensional quantum
systems, were recently proposed for generative modeling of natural data (such as images) in …

We present a variational renormalization group (RG) approach based on a reversible
generative model with hierarchical architecture. The model performs hierarchical change-of …

The resemblance between the methods used in quantum-many body physics and in
machine learning has drawn considerable attention. In particular, tensor networks (TNs) and …

Deep convolutional networks have witnessed unprecedented success in various machine
learning applications. Formal understanding on what makes these networks so successful is …

The classification of states of matter and their corresponding phase transitions is a special
kind of machine-learning task, where physical data allow for the analysis of new algorithms …