Book icon with persian name of Pubnito
  • Mağaza
  • Kütüphane
  • Sepetiniz


    Tüm nesneler:0

    Sepeti Görüntüle

    Tensor Network Contractions

    Tensor Network Contractions

    Shi-Ju RanEmanuele TirritoCheng PengXi ChenLuca TagliacozzoGang SuMaciej Lewenstein

    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.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.

    kitap formatı

    Özgür

    Yorumlar

    Derecelendirme Anlık Görüntüsü

    İncelemeleri filtrelemek için aşağıdan bir satır seçin.

    0

    0

    0

    0

    0

    0

    Genel

    Ortalama Müşteri Puanları

    Bu Kitap için İnceleme

    Düşüncelerinizi diğer okuyucularla paylaşın

    Daha Fazla Bilgi

    description_of_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 scienc

    Ek Bilgi

    SATICI

    Basımlar

    Yayın tarihi

    2020 Jul 01

    Yazarlar
    Shi-Ju RanEmanuele TirritoCheng PengXi ChenLuca TagliacozzoGang SuMaciej Lewenstein

    ISBN

    978-3-030-34489-4

    Yazarlar Hakkında

    Shi-Ju Ran
    Shi-Ju Ran

    Department of Physics Capital Normal University Beijing, China.  

      Shi-Ju Ran
      Emanuele Tirrito
      Emanuele Tirrito

      Quantum Optics Theory Institute of Photonic Sciences Castelldefels, Spain.  

      Emanuele Tirrito
      Cheng Peng
      Cheng Peng

      Stanford Institute for Materials and Energy Sciences SLAC and Stanford University Menlo Park, CA, USA.  

      Cheng Peng
      Xi Chen

      School of Physical Sciences University of Chinese Academy of Science Beijing, China.  

      Xi Chen
      Luca Tagliacozzo
      Luca Tagliacozzo

      Department of Quantum Physics and Astrophysics University of Barcelona Barcelona, Spain.  

      Luca Tagliacozzo
      Gang Su

      Kavli Institute for Theoretical Sciences University of Chinese Academy of Science Beijing, China.

      Gang Su
      Maciej Lewenstein
      Maciej Lewenstein

      Quantum Optics Theory Institute of Photonic Sciences Castelldefels, Spain.  

      Maciej Lewenstein

      İçindekiler

      logo

      Türkçe

      PUBNiTO tarafından desteklenmektedir | © 2024 Notion Wave A.Ş.