Tensor Network Contractions Book [PDF] Download

Download the fantastic book titled Tensor Network Contractions written by Shi-Ju Ran, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Tensor Network Contractions", which was released on 27 January 2020. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Science genre.

Summary of Tensor Network Contractions by Shi-Ju Ran PDF

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 big-data 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 many-body 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 non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.


Detail About Tensor Network Contractions PDF

  • Author : Shi-Ju Ran
  • Publisher : Springer Nature
  • Genre : Science
  • Total Pages : 160 pages
  • ISBN : 3030344894
  • PDF File Size : 16,8 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

Clicking on the GET BOOK button will initiate the downloading process of Tensor Network Contractions by Shi-Ju Ran. This book is available in ePub and PDF format with a single click unlimited downloads.

GET BOOK

Tensor Network Contractions

Tensor Network Contractions
  • Publisher : Springer Nature
  • File Size : 47,6 Mb
  • Release Date : 27 January 2020
GET BOOK

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

Tensor Network Contractions

Tensor Network Contractions
  • Publisher : Unknown Publisher
  • File Size : 35,7 Mb
  • Release Date : 08 October 2020
GET BOOK

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

Introduction to Tensor Network Methods

Introduction to Tensor Network Methods
  • Publisher : Springer
  • File Size : 38,5 Mb
  • Release Date : 28 November 2018
GET BOOK

This volume of lecture notes briefly introduces the basic concepts needed in any computational physics course: software and hardware, programming skills, linear algebra, and differential calculus. It then presents more

Tensor Networks for Dimensionality Reduction and Large-scale Optimization

Tensor Networks for Dimensionality Reduction and Large-scale Optimization
  • Publisher : Unknown Publisher
  • File Size : 20,8 Mb
  • Release Date : 09 June 2024
GET BOOK

Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning algorithms typically scale exponentially with

Tensor Networks for Dimensionality Reduction and Large-Scale Optimization

Tensor Networks for Dimensionality Reduction and Large-Scale Optimization
  • Publisher : Unknown Publisher
  • File Size : 21,5 Mb
  • Release Date : 28 May 2017
GET BOOK

This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost

Tensors: Geometry and Applications

Tensors: Geometry and Applications
  • Publisher : American Mathematical Soc.
  • File Size : 30,7 Mb
  • Release Date : 14 December 2011
GET BOOK

Tensors are ubiquitous in the sciences. The geometry of tensors is both a powerful tool for extracting information from data sets, and a beautiful subject in its own right. This

Group Theory

Group Theory
  • Publisher : Princeton University Press
  • File Size : 40,9 Mb
  • Release Date : 26 May 2020
GET BOOK

If classical Lie groups preserve bilinear vector norms, what Lie groups preserve trilinear, quadrilinear, and higher order invariants? Answering this question from a fresh and original perspective, Predrag Cvitanovic takes

Density Matrix and Tensor Network Renormalization

Density Matrix and Tensor Network Renormalization
  • Publisher : Cambridge University Press
  • File Size : 47,9 Mb
  • Release Date : 31 August 2023
GET BOOK

Renormalization group theory of tensor network states provides a powerful tool for studying quantum many-body problems and a new paradigm for understanding entangled structures of complex systems. In recent decades