Description 
1 online resource (xiv, 150 pages) : illustrations (some color) 
Series 
Lecture notes in physics ; v. 964 

Lecture notes in physics ; v. 964

Contents 
Introduction  Tensor Network: Basic Definitions and Properties  TwoDimensional Tensor Networks and Contraction Algorithms  Tensor Network Approaches for HigherDimensional Quantum Lattice Models  Tensor Network Contraction and MultiLinear Algebra  Quantum Entanglement Simulation Inspired by Tensor Network  Summary 
Summary 
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 quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the bigdata analysis. Tensor network originates from the numerical renormalization group approach proposed by K.G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum manybody physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for nonspecialists on quantum physics to understand tensor network algorithms and the related mathematics 
Bibliography 
Includes bibliographical references and index 
Subject 
Machine learning.


Manybody problem.


Particles (Nuclear physics)


Physics  Textbooks.


Quantum field theory.


Quantum optics.


Quantum theory.


Statistical physics.


Tensor algebra.


Machine learning.


Manybody problem.


Particles (Nuclear physics)


Physics.


Quantum field theory.


Quantum optics.


Quantum theory.


Statistical physics.


Tensor algebra.

Genre/Form 
Electronic books.


Textbooks.

Form 
Electronic book

Author 
Chen, Xi.


Lewenstein, Maciej


Peng, Cheng.


Su, Gang.


Tagliacozzo, Luca


Tirrito, Emanuele

ISBN 
3030344894 

9783030344894 
