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. Am...
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| Authors / Creators: | , |
|---|---|
| Other Authors / Creators: | Petruccione, Francesco. author. |
| Other Corporate Authors / Creators: | SpringerLink (Online service) |
| Format: | Electronic eBook |
| Language: | English |
| Edition: | 2nd ed. 2021. |
| Imprint: | Cham : Springer International Publishing : Imprint: Springer, 2021. |
| Series: | Quantum Science and Technology,
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| Subjects: | |
| Online Access: | Available in Springer Physics and Astronomy eBooks 2021 English/International. |
| LEADER | 02847cam a22004213i 4500 | ||
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| 008 | 211017s2021 sz | o |||| 0|eng d | ||
| 020 | |z 9783030830977 | ||
| 020 | |a 9783030830984 (online) | ||
| 035 | |a (EBZ)ebs29904040e | ||
| 040 | |d EBZ | ||
| 042 | |a msc | ||
| 050 | 4 | |a QA76.889 | |
| 100 | 1 | |a Schuld, Maria. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Machine Learning with Quantum Computers |h [electronic resource] |c by Maria Schuld, Francesco Petruccione. |
| 250 | |a 2nd ed. 2021. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2021. | |
| 490 | 1 | |a Quantum Science and Technology, |x 2364-9062 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Quantum computers. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 1 | 4 | |a Quantum Computing. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Mathematics and Computing. |
| 700 | 1 | |a Petruccione, Francesco. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Physics and Astronomy eBooks 2021 English/International |d Springer Nature | |
| 776 | 0 | 8 | |i Printed edition: |z 9783030830977 |
| 776 | 0 | 8 | |i Printed edition: |z 9783030830991 |
| 776 | 0 | 8 | |i Printed edition: |z 9783030831004 |
| 776 | 1 | |t Machine Learning with Quantum Computers | |
| 830 | 0 | |a Quantum Science and Technology, |x 2364-9062 | |
| 856 | 4 | 0 | |3 Full text available |z Available in Springer Physics and Astronomy eBooks 2021 English/International. |u https://ezproxy.wellesley.edu/login?url=https://link.springer.com/10.1007/978-3-030-83098-4 |