Machine Learning with Quantum Computers (Paperback, 2nd ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Quantum Science and Technology
Release date:	October 2022
First published:	2021
Authors:	Maria Schuld • Francesco Petruccione
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	312
Edition:	2nd ed. 2021
ISBN-13:	978-3-03-083100-4
Categories:	Books > Science & Mathematics > Mathematics > General Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-083100-0
Barcode:	9783030831004

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