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

Format:

Book

Author/Creator:

Schuld, Maria, author.

Petruccione, F. (Francesco), author.

Series:

Quantum science and technology 2364-9062

Quantum science and technology, 2364-9062

Language:

English

Subjects (All):

Machine learning.

Quantum computing.

Genre:

Electronic books.

Physical Description:

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

Edition:

Second edition.

Place of Publication:

Cham, Switzerland : Springer, 2021.

System Details:

text file

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

Online resource; title from PDF title page (SpringerLink, viewed October 20, 2021).

Other Format:

Print version:

ISBN:

9783030830984

3030830985

OCLC:

1277313999

Access Restriction:

Restricted for use by site license.