Supervised learning with quantum computers
type: Item
instance of: scholarly article
title: Supervised learning with quantum computers
zbMATH Open document ID: 1411.81008
DOI: 10.1007/978-3-319-96424-9
author: Maria Schuld
author: Francesco Petruccione
published in: Quantum Science and Technology
publication date: 2018-10-12T00:00:00Z
review text: The book provides a thorough introduction into the recently established field of quantum machine learning, which concerns itself with machine learning algorithms that are to be run on quantum computers. To this end it discusses how classical supervised learning algorithms can be ported to quantum computers such that their strengths are properly utilized. The book is intented as an extended introduction to this new research field and provides all required foundations both for the area of machine learning as well as quantum computing including the relevant knowledge and insights from quantum physics. In this manner, the book is relevant for computer science as well as physics graduates that each find the missing parts covered in the book. The advanced material covered in the later chapters includes proper data encoding as well as algorithms for training as well as inference, thus completing the pipeline of standard supervised machine learning approaches. The book starts with a detailed introduction that positions the field in the established areas and provides a gentle introductory example for a trivial learning task. This example motivates the different perspective as well as the benefits and the surprises involved with quantum computing. Chapter 2 provides a rather comprehensive high-level review of current supervised machine learning approaches including the discussion of the basic terminology used. Predictive models are introduced and it is demonstrated how they can be evaluated. This naturally leads to the notion of cost functions and standard training procedures based on loss. Finally, several common machine learning models are presented in this abstract setting with a strong focus on neural network and support vector machine approaches. Chapter 3 introduces quantum information theory and the foundations of quantum physics required to understand and appreciate quantum computation. This includes the basic storage unit as well as standard principles and common qua…
reviewed by: Andreas Maletti
Mathematics Subject Classification ID: 81-02
Mathematics Subject Classification ID: 81P68
Mathematics Subject Classification ID: 68Q05
Mathematics Subject Classification ID: 68Q12
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 82C32
zbMATH DE Number: 6954010
zbMATH Keywords: quantum computer
zbMATH Keywords: quantum learning
zbMATH Keywords: machine learning
zbMATH Keywords: neural networks
zbMATH Keywords: support vector machines
zbMATH Keywords: quantum data
Wikidata QID: Q60307391
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-319-96424-9
OpenAlex ID: W2888774813
description: scientific article; zbMATH DE number 6954010