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Machine Learning with Quantum Computers
~
Petruccione, Francesco.
Machine Learning with Quantum Computers
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Machine Learning with Quantum Computers/ by Maria Schuld, Francesco Petruccione.
Author:
Schuld, Maria.
other author:
Petruccione, Francesco.
Description:
XIV, 312 p. 104 illus., 74 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Quantum computers. -
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. - 2nd ed. 2021. - XIV, 312 p. 104 illus., 74 illus. in color.online resource. - 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:
564139
Quantum computers.
LC Class. No.: QA76.889
Dewey Class. No.: 004.1
Machine Learning with Quantum Computers
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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.
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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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