rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Introduction to tensor network methods
    sub title: from many-body quantum systems to machine learning
  name:
    name part: Felser, Timo
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Montangero, Simone
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: sz
    date issued: 2026
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxv, 332 pages) : illustrations.
  abstract: This second edition of the textbook Introduction to Tensor Network Methods contains more advanced and technical parts as new topics related to tensor network algorithms that have been developed in the last few years. The reader finds new chapters dedicated to tree tensor networks for high-dimensional systems as applications to lattice gauge theory. The implementation of tensor networks for machine learning is also presented in detail. This textbook gives an in-depth overview on the numerical simulation technique of tensor networks (TNs) with hands-on technical descriptions, work exercises and computation results. TNs have originally been developed for solving the quantum many-body problem and simulating quantum systems on a classical computer. However, as a mathematical tool, TNs have emerged as powerful theoretical and numerical versatile tools to attack more generally hard mathematical problems. In particular, their range application has expanded to combinatorial optimization and even as an alternative tool for machine learning in the field of artificial intelligence. This textbook introduces the reader to the field, describing the main principles and core mathematical concepts in the light of its application in quantum physics and, along the way, touches on the application of TNs to problems from various fields, ranging from low-energy to high-energy physics up to medical physics and machine learning. It is designed for graduate courses in computational physics, where a student learns how to write a tensor network program and can begin to explore the physics of many-body quantum systems.
  table of contents: Introduction -- Linear Algebra -- Numerical Calculus -- Numerical Renormalization Group Methods -- Tensor Network Methods -- Symmetric Tensor Networks -- Matrix Product States -- Tree Tensor Networks -- Augmented Tree Tensor Network -- Quantum Phase Transitions -- Hamiltonian Lattice Gauge Theories -- Out-of-equilibrium Processes -- Tensor Networks for Machine Learning.
  note: Timo Felser, Simone Montangero, editors.
  note: Includes bibliographical references.
  subject:
    topic: Machine learning
  subject:
    topic: Many-body problem
  subject:
    topic: Quantum theory
  subject:
    topic: Tensor fields
  classification: QA612 .I57 2026
  classification: 530.12
  related item:
    title info:
      title: Introduction to tensor network methods
    origin info:
      publisher: Cham : Springer, 2026
      edition: Second edition.
    identifier: (OCoLC)1592133298
  related item:
    title info:
      title: Graduate texts in physics
  identifier: 9783032176356 (electronic bk.)
  identifier: 3032176352
  identifier: 9783032176349
  identifier: 9783032176363
  identifier: 9783032176370
  location:
    physical location: POD
  record info:
    record content source: GW5XE
    record creation date: 260624
    record change date: 20260808204621.9
    record identifier: in00005790411
    language of cataloging:
      language term: eng