Schuld / Petruccione Machine Learning with Quantum Computers
2. Auflage 2021
ISBN: 978-3-030-83098-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 312 Seiten
Reihe: Physics and Astronomy
ISBN: 978-3-030-83098-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.




