Lingua: Inglese
Editore: Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
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Paperback. Condizione: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
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Condizione: New. 2nd ed. 2021 edition NO-PA16APR2015-KAP.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Machine Learning with Quantum Computers | Maria Schuld (u. a.) | Taschenbuch | Quantum Science and Technology | xiv | Englisch | 2022 | Springer | EAN 9783030831004 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 2nd edition. 326 pages. 9.25x6.10x0.75 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 parameterizedquantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks.The book aimsat 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.
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Lingua: Inglese
Editore: Springer Nature Switzerland AG, Cham, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Lingua: Inglese
Editore: Springer International Publishing Okt 2022, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 parameterizedquantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks.The book aimsat 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. 328 pp. Englisch.
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Lingua: Inglese
Editore: Springer, Springer International Publishing Okt 2022, 2022
ISBN 10: 3030831000 ISBN 13: 9783030831004
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 328 pp. Englisch.