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Quantum Machine Learning: What Quantum Computing Means to Data Mining
University of Borås, Swedish School of Library and Information Science.
2014 (English)Book (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Academic Press , 2014. , 176 p.
Keyword [en]
machine learning, quantum computing, supervised learning, unsupervised learning, process tomography, quantum learning of unitary, support vector machines, adiabatic processes, neural networks, Hopfield networks, quantum optimization, clustering, Quantum Information Theory
National Category
Computer and Information Science
Research subject
Library and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-3700Local ID: 2320/14000ISBN: 9780128009536 (print)OAI: oai:DiVA.org:hb-3700DiVA: diva2:877090
Available from: 2015-12-04 Created: 2015-12-04

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