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 …

Quantum computers are promising for simulations of chemical and physical systems, but the
limited capabilities of today's quantum processors permit only small, and often approximate …

A core technology that has emerged from the artificial intelligence revolution is the recurrent
neural network (RNN). Its unique sequence-based architecture provides a tractable …

In the past two decades, the density matrix renormalization group (DMRG) has emerged as
an innovative new method in quantum chemistry relying on a theoretical framework very …

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 …

Tensor network is a fundamental mathematical tool with a huge range of applications in
physics, such as condensed matter physics, statistic physics, high energy physics, and …

Neural-network quantum states have recently been introduced as an Ansatz for describing
the wave function of quantum many-body systems. We show that there are strong …

Featuring detailed explanations of the major algorithms used in quantum Monte Carlo
simulations, this is the first textbook of its kind to provide a pedagogical overview of the field …

We introduce a method to measure many-body magic in quantum systems based on a
statistical exploration of Pauli strings via Markov chains. We demonstrate that sampling such …