Quantum Science and Technology
cover

Supervised Learning with Quantum Computers

Autoren: Schuld, Maria, Petruccione, Francesco

  • Explains relevant concepts and terminology from machine learning and quantum information in an accessible language
  • Introduces a structure into the literature by clustering the work in terms of what aspects of quantum information are exploited to advance machine learning
  • Critically reviews challenges that are a common theme in the works
  • Gives a comprehensive outlook on future directions
Weitere Vorteile

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eBook 93,08 €
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  • ISBN 978-3-319-96424-9
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Preis für Deutschland (Brutto)
Softcover 117,69 €
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Über dieses Buch

Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.

Über die Autor*innen

Francesco Petruccione received his PhD (1988) and ”Habilitation” (1994) from the University of Freiburg, Germany. Since 2004 he is Professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, Africa, where in 2007 he was granted a South African Research Chair for Quantum Information Processing and Communication from the National Research Foundation. He is the co-author of “The theory of open quantum systems” (Oxford University Press, 2002) and has published more than 100 papers in refereed journals, adding up to more than 7000 citations. Francesco Petruccione’s research focusses on quantum information and open quantum systems.
Maria Schuld received her PhD degree from the University of KwaZulu-Natal in South Africa in 2017 as a fellow of the German Academic Foundation. Her Master’s degree was awarded by the Technical University of Berlin and supported through a scholarship of the German Academic Exchange Service (DAAD). Since 2013 she dedicates her research to the design of quantum machine learning algorithms, which she presented at numerous international conferences and in a range of research articles. Maria Schuld is a Post-Doc at the University of KwaZulu-Natal and works as a researcher for the Canadian-based quantum computing startup Xanadu.

Stimmen zum Buch

“The book is very well written and contains sufficiently many examples and illustrations. The authors make a concerted effort to make the material accessible to both computer science graduates as well as scientists with a quantum physics background. … The intended audience are thus machine learning scientists that want to explore the quantum approach to their discipline or quantum information scientists that want to enter the field of machine learning.” (Andreas Maletti, zbMATH 1411.81008, 2019)

Inhaltsverzeichnis (9 Kapitel)

Inhaltsverzeichnis (9 Kapitel)
  • Introduction

    Seiten 1-19

    Schuld, Maria (et al.)

  • Machine Learning

    Seiten 21-73

    Schuld, Maria (et al.)

  • Quantum Information

    Seiten 75-125

    Schuld, Maria (et al.)

  • Quantum Advantages

    Seiten 127-137

    Schuld, Maria (et al.)

  • Information Encoding

    Seiten 139-171

    Schuld, Maria (et al.)

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-96424-9
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF, EPUB
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 160,49 €
Preis für Deutschland (Brutto)
Softcover 117,69 €
Preis für Deutschland (Brutto)
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Bibliografische Information

Bibliographic Information
Buchtitel
Supervised Learning with Quantum Computers
Autoren
Titel der Buchreihe
Quantum Science and Technology
Copyright
2018
Verlag
Springer International Publishing
Copyright Inhaber
Springer Nature Switzerland AG
eBook ISBN
978-3-319-96424-9
DOI
10.1007/978-3-319-96424-9
Hardcover ISBN
978-3-319-96423-2
Softcover ISBN
978-3-030-07188-2
Buchreihen ISSN
2364-9054
Auflage
1
Seitenzahl
XIII, 287
Anzahl der Bilder
35 schwarz-weiß Abbildungen, 48 Abbildungen in Farbe
Themen