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Autor(en): 
  • Cheng Peng
  • XI Chen
  • Maciej Lewenstein
  • Gang Su
  • Shi-Ju Ran
  • Luca Tagliacozzo
  • Emanuele Tirrito
  • Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Januar 2020  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Mathematische / Computergestützte / Theoretische Physik / multi-linearalgebra / Openaccessbook / Optische Physik / Quantenphysik (Quantenmechanik und Quantenfeldtheorie) / quantumcircuits / QuantumEntanglement / quantumsimulationsinmany-bodysystems
    ISBN:  9783030344887 
    EAN-Code: 
    9783030344887 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 10 mm 
    Gewicht:  260 gr 
    Seiten:  164 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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 notwell 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.

      



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