Machine learning with quantum computers

by Schuld , Maria

Type Book

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Title Statements

Full title :Machine learning with quantum computers / Maria Schuld, Francesco Petruccione.

Summary

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

Edition Statements

Publication Date:2021.
Edition:Second edition

Publication Statements

Publisher(s):Springer
Place of Publication:Cham, Switzerland
Language:eng
Publication Date:2021.

The Physical Object

Description:1 online resource (xiv, 312 pages) : : illustrations (some color)

Edition Identifiers

ISBN:9783030830984, 3030830985
DOI:10.1007/978-3-030-83098-4
oclc:on1277313999
LC Classification:Q325.5
Dewey Decimal:006.3/1

Introduction -- Machine learning -- Quantum computing -- Representing data on a quantum computer -- Variational circuits as machine learning models -- Quantum models as Kernel methods -- Fault-tolerant quantum machine learning -- Approaches based on the Ising model -- Potential quantum advantages.