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| # |
Author | Title | Accn# | Year | Item Type | Claims |
| 1 |
Schuld, Maria |
Machine Learning with Quantum Computers |
I11858 |
2021 |
eBook |
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| 2 |
Schuld, Maria |
Supervised Learning with Quantum Computers |
I09145 |
2018 |
eBook |
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1.
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| Title | Machine Learning with Quantum Computers |
| Author(s) | Schuld, Maria;Petruccione, Francesco |
| Publication | Cham, Springer International Publishing, 2021. |
| Description | XIV, 312 p. 104 illus., 74 illus. in color : online resource |
| Abstract Note | 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,Price | 9783030830984 |
| Keyword(s) | 1. EBOOK
2. EBOOK - SPRINGER
3. MACHINE LEARNING
4. MATHEMATICS
5. Mathematics and Computing
6. QUANTUM COMPUTERS
7. Quantum computing
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| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I11858 |
|
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On Shelf |
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2.
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| Title | Supervised Learning with Quantum Computers |
| Author(s) | Schuld, Maria;Petruccione, Francesco |
| Publication | Cham, Springer International Publishing, 2018. |
| Description | XIII, 287 p. 83 illus., 48 illus. in color : online resource |
| Abstract Note | Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices |
| ISBN,Price | 9783319964249 |
| Keyword(s) | 1. ARTIFICIAL INTELLIGENCE
2. EBOOK
3. EBOOK - SPRINGER
4. Numerical and Computational Physics, Simulation
5. PATTERN RECOGNITION
6. PHYSICS
7. QUANTUM COMPUTERS
8. Quantum computing
9. Quantum Information Technology, Spintronics
10. QUANTUM PHYSICS
11. SPINTRONICS
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09145 |
|
|
On Shelf |
|
|
|
| |