國立虎尾科技大學 |

Tensor Network Contractions = Methods and Applications to Quantum Many-Body Systems /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Tensor Network Contractions/ by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein.
Reminder of title:	Methods and Applications to Quantum Many-Body Systems /
Author:	Ran, Shi-Ju.
other author:	Lewenstein, Maciej.
Description:	XIV, 150 p. 68 illus., 65 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Elementary Particles, Quantum Field Theory. -
Online resource:	https://doi.org/10.1007/978-3-030-34489-4
ISBN:	9783030344894

Tensor Network Contractions = Methods and Applications to Quantum Many-Body Systems /
Ran, Shi-Ju.

Tensor Network ContractionsMethods and Applications to Quantum Many-Body Systems /[electronic resource] :by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein. - 1st ed. 2020. - XIV, 150 p. 68 illus., 65 illus. in color.online resource. - Lecture Notes in Physics,9640075-8450 ;. - Lecture Notes in Physics,891.

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.

Open Access

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.

ISBN: 9783030344894

Standard No.: 10.1007/978-3-030-34489-4doiSubjects--Topical Terms:

672693
Elementary Particles, Quantum Field Theory.

LC Class. No.: QC5.53

Dewey Class. No.: 530.15

Tensor Network Contractions = Methods and Applications to Quantum Many-Body Systems /
LDR:03886nam a22004335i 4500 001 1017934
003 DE-He213
005 20200701155835.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030344894 $9 978-3-030-34489-4
024 7 $a 10.1007/978-3-030-34489-4 $2 doi
035 $a 978-3-030-34489-4
050 4 $a QC5.53
072 7 $a PHU $2 bicssc
072 7 $a SCI040000 $2 bisacsh
072 7 $a PHU $2 thema
082 0 4 $a 530.15 $2 23
100 1 $a Ran, Shi-Ju. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1312835
245 1 0 $a Tensor Network Contractions $h [electronic resource] : $b Methods and Applications to Quantum Many-Body Systems / $c by Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Luca Tagliacozzo, Gang Su, Maciej Lewenstein.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIV, 150 p. 68 illus., 65 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Physics, $x 0075-8450 ; $v 964
505 0 $a 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.
506 0 $a Open Access
520 $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 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.
650 2 4 $a Elementary Particles, Quantum Field Theory. $3 672693
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistical Physics and Dynamical Systems. $3 1114011
650 2 4 $a Quantum Optics. $3 768682
650 2 4 $a Quantum Physics. $3 671960
650 1 4 $a Mathematical Methods in Physics. $3 670749
650 0 $a Quantum field theory. $3 579915
650 0 $a Elementary particles (Physics). $3 1254811
650 0 $a Machine learning. $3 561253
650 0 $a Statistical physics. $3 528048
650 0 $a Quantum optics. $3 593075
650 0 $a Quantum physics. $3 1179090
650 0 $a Physics. $3 564049
700 1 $a Lewenstein, Maciej. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1226262
700 1 $a Su, Gang. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1312838
700 1 $a Tagliacozzo, Luca. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1312837
700 1 $a Chen, Xi. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1022407
700 1 $a Peng, Cheng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1193551
700 1 $a Tirrito, Emanuele. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1312836
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030344887
776 0 8 $i Printed edition: $z 9783030344900
830 0 $a Lecture Notes in Physics, $x 0075-8450 ; $v 891 $3 1253935
856 4 0 $u https://doi.org/10.1007/978-3-030-34489-4
912 $a ZDB-2-PHA
912 $a ZDB-2-SXP
912 $a ZDB-2-LNP
912 $a ZDB-2-SOB
950 $a Physics and Astronomy (SpringerNature-11651)
950 $a Physics and Astronomy (R0) (SpringerNature-43715)