Quantum Machine Learning: A tutorial

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technolog...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 470; s. 457 - 461
Hlavní autoři: Martín-Guerrero, José D., Lamata, Lucas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 22.01.2022
Témata:
Computational speed-up
Quantum autoencoders
Quantum clustering
Quantum computing
Quantum reinforcement learning
Quantum-inspired learning algorithms
Quantum-inspired learning algorithms
Quantum clustering
Quantum reinforcement learning
Quantum computing
Quantum autoencoders
Computational speed-up
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2021.02.102