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 …

ITensor is a system for programming tensor network calculations with an interface modeled
on tensor diagram notation, which allows users to focus on the connectivity of a tensor …

Matrix-product states have become the de facto standard for the representation of one-
dimensional quantum many body states. During the last few years, numerous new methods …

Tensor product state (TPS) based methods are powerful tools to efficiently simulate quantum
many-body systems in and out of equilibrium. In particular, the one-dimensional matrix …

The treatment of high‐dimensional problems such as the Schrödinger equation can be
approached by concepts of tensor product approximation. We present general techniques …

During the past 15 years, the density matrix renormalization group (DMRG) has become
increasingly important for ab initio quantum chemistry. Its underlying wavefunction ansatz …

In the last years, a number of theoretical and numerical tools have been developed by a
thriving community formed by people coming from different backgrounds—condensed …

We present a compendium of numerical simulation techniques, based on tensor network
methods, aiming to address problems of many-body quantum mechanics on a classical …

Google's tensor processing units (TPUs) are integrated circuits specifically built to accelerate
and scale up machine learning workloads. They can perform fast distributed matrix …