國立高雄大學圖資館 |

Language: English

Back

Tensor network contractionsmethods a...

Ran, Shi-Ju.

Tensor network contractionsmethods and applications to quantum many-body systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tensor network contractionsby Shi-Ju Ran ... [et al.].
Reminder of title:	methods and applications to quantum many-body systems /
other author:	Ran, Shi-Ju.
Published:	Cham :Springer International Publishing :2020.
Description:	xiv, 150 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Many-body problem.
Online resource:	https://doi.org/10.1007/978-3-030-34489-4
ISBN:	9783030344894$q(electronic bk.)

Tensor network contractionsmethods and applications to quantum many-body systems /
Tensor network contractionsmethods and applications to quantum many-body systems /[electronic resource] :by Shi-Ju Ran ... [et al.]. - Cham :Springer International Publishing :2020. - xiv, 150 p. :ill. (some col.), digital ;24 cm. - Lecture notes in physics,v.9640075-8450 ;. - Lecture notes in physics ;650..

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$q(electronic bk.)

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

207887
Many-body problem.

LC Class. No.: QC174.17.P7

Dewey Class. No.: 530.15

Tensor network contractionsmethods and applications to quantum many-body systems /
LDR:03438nmm a2200349 a 4500 001 573758
003 DE-He213
005 20200703151117.0
006 m d
007 cr nn 008maaau
008 200928s2020 sz s 0 eng d
020 $a 9783030344894$q(electronic bk.)
020 $a 9783030344887$q(paper)
024 7 $a 10.1007/978-3-030-34489-4 $2 doi
035 $a 978-3-030-34489-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QC174.17.P7
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
090 $a QC174.17.P7 $b T312 2020
245 0 0 $a Tensor network contractions $h [electronic resource] : $b methods and applications to quantum many-body systems / $c by Shi-Ju Ran ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiv, 150 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in physics, $x 0075-8450 ; $v 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 $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 0 $a Many-body problem. $3 207887
650 0 $a Physics $3 218415
650 0 $a Tensor algebra. $3 348467
650 0 $a Quantum theory. $3 199020
650 0 $a Quantum optics. $3 200591
650 0 $a Statistical physics. $3 183716
650 0 $a Machine learning. $3 188639
650 0 $a Particles (Nuclear physics) $3 204127
650 0 $a Quantum field theory. $3 203819
650 1 4 $a Mathematical Methods in Physics. $3 273796
650 2 4 $a Quantum Physics. $3 275010
650 2 4 $a Quantum Optics. $3 376759
650 2 4 $a Statistical Physics and Dynamical Systems. $3 760415
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Elementary Particles, Quantum Field Theory. $3 274994
700 1 $a Ran, Shi-Ju. $3 861158
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Lecture notes in physics ; $v 650. $3 451204
856 4 0 $u https://doi.org/10.1007/978-3-030-34489-4
950 $a Physics and Astronomy (Springer-11651)