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Machine learning with quantum computers

Schuld, Maria.

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Machine learning with quantum computers

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning with quantum computers/ by Maria Schuld, Francesco Petruccione.
Author:	Schuld, Maria.
other author:	Petruccione, F.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiv, 312 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Machine Learning -- Chapter 3. Quantum Computing -- Chapter 4. Representing Data on a Quantum Computer -- Chapter 5. Variational Circuits as Machine Learning Models -- Chapter 6. Quantum Models as Kernel Methods -- Chapter 7. Fault-Tolerant Quantum Machine Learning -- Chapter 8. Approaches Based on the Ising Model -- Chapter 9. Potential Quantum Advantages.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-83098-4
ISBN:	9783030830984

Machine learning with quantum computers
Schuld, Maria.

Machine learning with quantum computers[electronic resource] /by Maria Schuld, Francesco Petruccione. - Second edition. - Cham :Springer International Publishing :2021. - xiv, 312 p. :ill. (some col.), digital ;24 cm. - Quantum science and technology,2364-9062. - Quantum science and technology..

Chapter 1. Introduction -- Chapter 2. Machine Learning -- Chapter 3. Quantum Computing -- Chapter 4. Representing Data on a Quantum Computer -- Chapter 5. Variational Circuits as Machine Learning Models -- Chapter 6. Quantum Models as Kernel Methods -- Chapter 7. Fault-Tolerant Quantum Machine Learning -- Chapter 8. Approaches Based on the Ising Model -- Chapter 9. Potential Quantum Advantages.

This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

ISBN: 9783030830984

Standard No.: 10.1007/978-3-030-83098-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .S34 2021

Dewey Class. No.: 006.31

Machine learning with quantum computers
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245 1 0 $a Machine learning with quantum computers $h [electronic resource] / $c by Maria Schuld, Francesco Petruccione.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 312 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Quantum science and technology, $x 2364-9062
505 0 $a Chapter 1. Introduction -- Chapter 2. Machine Learning -- Chapter 3. Quantum Computing -- Chapter 4. Representing Data on a Quantum Computer -- Chapter 5. Variational Circuits as Machine Learning Models -- Chapter 6. Quantum Models as Kernel Methods -- Chapter 7. Fault-Tolerant Quantum Machine Learning -- Chapter 8. Approaches Based on the Ising Model -- Chapter 9. Potential Quantum Advantages.
520 $a This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
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650 2 4 $a Mathematics, general. $3 895821
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856 4 0 $u https://doi.org/10.1007/978-3-030-83098-4
950 $a Physics and Astronomy (SpringerNature-11651)