Machine Learning with Quantum Computers - Francesco Petruccione,Maria Schuld
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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 an ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Francesco Petruccione, Maria Schuld |
|---|---|
| Julkaisija | Springer Nature Switzerland |
| Series | Quantum Science and Technology |
| Julkaisuvuosi | 2022 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783030831004 |