Supervised Learning with Quantum Computers

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

192,59 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Aktuell nicht verfügbar

Kategorien:

Der Artikel ist nicht in deutscher Sprache verfasst.
Die Sprache der Produktbeschreibung und Details kann in Englisch angegeben sein. Sollten Sie Verständnisprobleme haben, wenden Sie sich gerne an uns.

Beschreibung

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.

Über den Autor

Francesco Petruccione received his PhD (1988) and "Habilitation" (1994) from the University of Freiburg, Germany. Since 2004 he is Professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, Africa, where in 2007 he was granted a South African Research Chair for Quantum Information Processing and Communication from the National Research Foundation. He is the co-author of "The theory of open quantum systems" (Oxford University Press, 2002) and has published more than 100 papers in refereed journals, adding up to more than 7000 citations. Francesco Petruccione's research focusses on quantum information and open quantum systems.

Maria Schuld received her PhD degree from the University of KwaZulu-Natal in South Africa in 2017 as a fellow of the German Academic Foundation. Her Master's degree was awarded by the Technical University of Berlin and supported through a scholarship of the German Academic Exchange Service (DAAD). Since 2013 she dedicates her research to the design of quantum machine learning algorithms, which she presented at numerous international conferences and in a range of research articles. Maria Schuld is a Post-Doc at the University of KwaZulu-Natal and works as a researcher for the Canadian-based quantum computing startup Xanadu.

Zusammenfassung

Explains relevant concepts and terminology from machine learning and quantum information in an accessible language

Introduces a structure into the literature by clustering the work in terms of what aspects of quantum information are exploited to advance machine learning

Critically reviews challenges that are a common theme in the works

Gives a comprehensive outlook on future directions

Inhaltsverzeichnis

Introduction.- Background.- How quantum computers can classify data.- Organisation of the book.- Machine Learning.- Prediction.- Models.- Training.- Methods in machine learning.- Quantum Information.- Introduction to quantum theory.- Introduction to quantum computing.- An example: The Deutsch-Josza algorithm.- Strategies of information encoding.- Important quantum routines.- Quantum advantages.- Computational complexity of learning.- Sample complexity.- Model complexity.- Information encoding.- Basis encoding.- Amplitude encoding.- Qsample encoding.- Hamiltonian encoding.- Quantum computing for inference.- Linear models.- Kernel methods.- Probabilistic models.- Quantum computing for training.- Quantum blas.- Search and amplitude amplification.- Hybrid training for variational algorithms.- Quantum adiabatic machine learning.- Learning with quantum models.- Quantum extensions of Ising-type models.- Variational classifiers and neural networks.- Other approaches to buildquantum models.- Prospects for near-term quantum machine learning.- Small versus big data.- Hybrid versus fully coherent approaches.- Qualitative versus quantitative advantages.- What machine learning can do for quantum computing.- References.

Details

Medium:	Buch
Reihe:	Quantum Science and Technology
Inhalt:	xiii 287 S. 35 s/w Illustr. 48 farbige Illustr. 287 p. 83 illus. 48 illus. in color.
ISBN-13:	9783319964232
ISBN-10:	3319964232
Sprache:	Englisch
Herstellernummer:	978-3-319-96423-2
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Petruccione, Francesco Schuld, Maria
Auflage:	1st ed. 2018
Hersteller:	Springer International Publishing Springer International Publishing AG Quantum Science and Technology
Maße:	241 x 160 x 22 mm
Von/Mit:	Francesco Petruccione (u. a.)
Erscheinungsdatum:	22.09.2018
Gewicht:	0,623 kg

Artikel-ID: 113973745