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: Many-sorted algebras for deep learning and quantum technology
  name:
    name part: Girardina, Charles R.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    place:
      place term: Cambridge, MA
    publisher: MORGAN KAUFMANN
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (350 pages) : illustrations
  abstract: Many-Sorted Algebras for Deep Learning and Quantum Technology presents a precise and rigorous description of basic concepts in Quantum technologies and how they relate to Deep Learning and Quantum Theory. Current merging of Quantum Theory and Deep Learning techniques provides a need for a text that can give readers insight into the algebraic underpinnings of these disciplines. Although analytical, topological, probabilistic, as well as geometrical concepts are employed in many of these areas, algebra exhibits the principal thread. This thread is exposed using Many-Sorted Algebras (MSA). In almost every aspect of Quantum Theory as well as Deep Learning more than one sort or type of object is involved. For instance, in Quantum areas Hilbert spaces require two sorts, while in affine spaces, three sorts are needed. Both a global level and a local level of precise specification is described using MSA. At a local level operation involving neural nets may appear to be very algebraically different than those used in Quantum systems, but at a global level they may be identical. Again, MSA is well equipped to easily detail their equivalence through text as well as visual diagrams. Among the reasons for using MSA is in illustrating this sameness. Author Charles R. Giardina includes hundreds of well-designed examples in the text to illustrate the intriguing concepts in Quantum systems. Along with these examples are numerous visual displays. In particular, the Polyadic Graph shows the types or sorts of objects used in Quantum or Deep Learning. It also illustrates all the inter and intra sort operations needed in describing algebras. In brief, it provides the closure conditions. Throughout the text, all laws or equational identities needed in specifying an algebraic structure are precisely described. Includes hundreds of well-designed examples to illustrate the intriguing concepts in quantum systems Provides precise description of all laws or equational identities that are needed…
  note: Charles R. Giradina.
  note: Includes bibliographical references and index.
  subject:
    topic: Computer science
    topic: Mathematics
  subject:
    topic: Machine learning
  subject:
    topic: Quantum computing
  subject:
    topic: Informatique
    topic: Mathématiques
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Informatique quantique
  classification: QA76.9.M35 G57 2024
  classification: 004.01/51
  related item:
    identifier: (OCoLC)1389876819
  identifier: 9780443136986
  identifier: 044313698X
  identifier: 0443136971
  identifier: 9780443136979
  identifier: 9780443136986 O'Reilly Media
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 240206
    record change date: 20260327175529.4
    record identifier: in00005772895
    language of cataloging:
      language term: eng