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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.
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.
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.
Francesco Petruccione received his Ph.D. (1988) and ¿Habilitation¿ (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of ¿The Theory of Open Quantum Systems¿ (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione¿s research focuses on open quantum systems and quantum information processing and communication.
Explains relevant concepts and terminology from machine learning and quantum information
Critically reviews challenges that are a common theme in the literature
Focuses on the developments in near-term quantum machine learning in this second edition
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.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Theoretische Physik |
| Genre: | Physik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Reihe: | Quantum Science and Technology |
| Inhalt: |
xiv
312 S. 30 s/w Illustr. 74 farbige Illustr. 312 p. 104 illus. 74 illus. in color. |
| ISBN-13: | 9783030831004 |
| ISBN-10: | 3030831000 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Petruccione, Francesco
Schuld, Maria |
| Auflage: | 2nd ed. 2021 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Quantum Science and Technology |
| Maße: | 235 x 155 x 18 mm |
| Von/Mit: | Francesco Petruccione (u. a.) |
| Erscheinungsdatum: | 19.10.2022 |
| Gewicht: | 0,499 kg |
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.
Francesco Petruccione received his Ph.D. (1988) and ¿Habilitation¿ (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of ¿The Theory of Open Quantum Systems¿ (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione¿s research focuses on open quantum systems and quantum information processing and communication.
Explains relevant concepts and terminology from machine learning and quantum information
Critically reviews challenges that are a common theme in the literature
Focuses on the developments in near-term quantum machine learning in this second edition
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.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Theoretische Physik |
| Genre: | Physik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Reihe: | Quantum Science and Technology |
| Inhalt: |
xiv
312 S. 30 s/w Illustr. 74 farbige Illustr. 312 p. 104 illus. 74 illus. in color. |
| ISBN-13: | 9783030831004 |
| ISBN-10: | 3030831000 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Petruccione, Francesco
Schuld, Maria |
| Auflage: | 2nd ed. 2021 |
| Hersteller: |
Springer International Publishing
Springer International Publishing AG Quantum Science and Technology |
| Maße: | 235 x 155 x 18 mm |
| Von/Mit: | Francesco Petruccione (u. a.) |
| Erscheinungsdatum: | 19.10.2022 |
| Gewicht: | 0,499 kg |
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