| Dewey Class |
530.15 |
| Title |
Tensor Network Contractions : Methods and Applications to Quantum Many-Body Systems ([Ebook]) / by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein. |
| Author |
Ran, Shi-Ju |
| Added Personal Name |
Tirrito, Emanuele |
| Peng, Cheng |
| Other name(s) |
SpringerLink (Online service) |
| Publication |
Cham, Switzerland AG : Springer International Publishing , 2020 |
| Physical Details |
XIV, 150 pages: 68 illus., 65 illus. in color.) : Online resource |
| Series |
Lecture notes in physics ; 964 |
| ISBN |
978-3-030-34488-7 |
| Note |
Copy Rights -The Editor(s) (if applicable) and the Author(s) 2020. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Summary Note |
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.: |
| Contents note |
Introduction -- Tensor Network: Basic Definitions and Properties -- Two-Dimensional Tensor Networks and Contraction Algorithms -- Tensor Network Approaches for Higher-Dimensional Quantum Lattice Models -- Tensor Network Contraction and Multi-Linear Algebra -- Quantum Entanglement Simulation Inspired by Tensor Network -- Summary. |
| Mode of acces to digital resource |
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Open Access digital book. It is licensed under the terms of the Creative Commons Attribution 4.0 International License |
| Internet Site |
https://doi.org/10.1007/978-3-030-34489-4 |
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