Machine Learning with Quantum Computers
(Sprache: Englisch)
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...
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Klappentext zu „Machine Learning with Quantum Computers “
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.
Inhaltsverzeichnis zu „Machine Learning with Quantum Computers “
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.
Autoren-Porträt von Maria Schuld, Francesco Petruccione
Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane. Bibliographische Angaben
- Autoren: Maria Schuld , Francesco Petruccione
- 2021, 2. Aufl., XIV, 312 Seiten, 74 farbige Abbildungen, Masse: 16,1 x 24,6 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3030830977
- ISBN-13: 9783030830977
Sprache:
Englisch
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