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Supervised learning with quantum com...

Petruccione, Francesco.

Supervised learning with quantum computers

Record Type:	Electronic resources : Monograph/item
Title/Author:	Supervised learning with quantum computersby Maria Schuld, Francesco Petruccione.
Author:	Schuld, Maria.
other author:	Petruccione, Francesco.
Published:	Cham :Springer International Publishing :2018.
Description:	xiii, 287 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Quantum theory.
Online resource:	https://doi.org/10.1007/978-3-319-96424-9
ISBN:	9783319964249$q(electronic bk.)

Supervised learning with quantum computers
Schuld, Maria.

Supervised learning with quantum computers[electronic resource] /by Maria Schuld, Francesco Petruccione. - Cham :Springer International Publishing :2018. - xiii, 287 p. :ill., digital ;24 cm. - Quantum science and technology,2364-9054. - Quantum science and technology..

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 build quantum 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.

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: 9783319964249$q(electronic bk.)

Standard No.: 10.1007/978-3-319-96424-9doiSubjects--Topical Terms:

199020
Quantum theory.

LC Class. No.: Q325.5 / .S385 2018

Dewey Class. No.: 006.31

Supervised learning with quantum computers
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505 0 $a 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 build quantum 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.
520 $a 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.
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