Quantum Machine Learning

What Quantum Computing Means to Data Mining

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Science & Nature, Science, Physics, Quantum Theory
Cover of the book Quantum Machine Learning by Peter Wittek, Elsevier Science
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Author: Peter Wittek ISBN: 9780128010990
Publisher: Elsevier Science Publication: September 10, 2014
Imprint: Academic Press Language: English
Author: Peter Wittek
ISBN: 9780128010990
Publisher: Elsevier Science
Publication: September 10, 2014
Imprint: Academic Press
Language: English

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research.

Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.

  • Bridges the gap between abstract developments in quantum computing with the applied research on machine learning
  • Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing
  • Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research.

Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.

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