Originally developed in the context of condensed-matter physics and based on
renormalization group ideas, tensor networks have been revived thanks to quantum …

Tensor networks provide extremely powerful tools for the study of complex classical and
quantum many-body problems. Over the past two decades, the increment in the number of …

Lattice gauge theories, which originated from particle physics in the context of Quantum
Chromodynamics (QCD), provide an important intellectual stimulus to further develop …

A quantum many-body system whose dynamics includes local measurements at a nonzero
rate can be in distinct dynamical phases, with differing entanglement properties. We …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

This review focuses on the field of quantum entanglement applied to condensed matter
physics systems with strong correlations, a domain which has rapidly grown over the last …

Abstract Machine learning is a promising application of quantum computing, but challenges
remain for implementation today because near-term devices have a limited number of …

This is a partly non-technical introduction to selected topics on tensor network methods,
based on several lectures and introductory seminars given on the subject. It should be a …

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 …

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep
learning. RBM finds wide applications in dimensional reduction, feature extraction, and …