Machine Learning with Quantum Computers

,

Éditeur :

Springer

Paru le : 2021-10-17

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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Éditeur

Collection
n.c

Parution
2021-10-17

Pages
312 pages

EAN papier
9783030830977

Auteur(s) du livre


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.

Caractéristiques détaillées - droits

EAN PDF
9783030830984
Prix
126,59 €
Nombre pages copiables
3
Nombre pages imprimables
31
Taille du fichier
7905 Ko
EAN EPUB
9783030830984
Prix
126,59 €
Nombre pages copiables
3
Nombre pages imprimables
31
Taille du fichier
23951 Ko

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