Overview
- 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
Part of the book series: Quantum Science and Technology (QST)
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About this book
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.
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Keywords
- quantum phase estimation
- quantum walks
- quantum annealing
- hidden Markov models
- belief nets
- Boltzmann machines
- adiabatic quantum computing
- Grover search
- Hopfield models
- Quantum inference
- Artificial neural network
- near term application
- Quantum machine learning
- data driven prediction
- Qsample encoding
- quantum gates
- Deutsch-Josza algorithm
- Kernel methods
- quantum blas
Table of contents (9 chapters)
Reviews
“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)
Authors and Affiliations
About the authors
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.
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.
Bibliographic Information
Book Title: Supervised Learning with Quantum Computers
Authors: Maria Schuld, Francesco Petruccione
Series Title: Quantum Science and Technology
DOI: https://doi.org/10.1007/978-3-319-96424-9
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-96423-2Published: 22 September 2018
Softcover ISBN: 978-3-030-07188-2Published: 03 January 2019
eBook ISBN: 978-3-319-96424-9Published: 30 August 2018
Series ISSN: 2364-9054
Series E-ISSN: 2364-9062
Edition Number: 1
Number of Pages: XIII, 287
Number of Illustrations: 35 b/w illustrations, 48 illustrations in colour
Topics: Quantum Physics, Quantum Computing, Pattern Recognition, Quantum Information Technology, Spintronics, Numerical and Computational Physics, Simulation, Artificial Intelligence