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Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806018/ https://www.ncbi.nlm.nih.gov/pubmed/29434508 http://dx.doi.org/10.1098/rspa.2017.0551 |
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author | Ciliberto, Carlo Herbster, Mark Ialongo, Alessandro Davide Pontil, Massimiliano Rocchetto, Andrea Severini, Simone Wossnig, Leonard |
author_facet | Ciliberto, Carlo Herbster, Mark Ialongo, Alessandro Davide Pontil, Massimiliano Rocchetto, Andrea Severini, Simone Wossnig, Leonard |
author_sort | Ciliberto, Carlo |
collection | PubMed |
description | Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. |
format | Online Article Text |
id | pubmed-5806018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-58060182018-02-12 Quantum machine learning: a classical perspective Ciliberto, Carlo Herbster, Mark Ialongo, Alessandro Davide Pontil, Massimiliano Rocchetto, Andrea Severini, Simone Wossnig, Leonard Proc Math Phys Eng Sci Review Articles Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. The Royal Society Publishing 2018-01 2018-01-17 /pmc/articles/PMC5806018/ /pubmed/29434508 http://dx.doi.org/10.1098/rspa.2017.0551 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Review Articles Ciliberto, Carlo Herbster, Mark Ialongo, Alessandro Davide Pontil, Massimiliano Rocchetto, Andrea Severini, Simone Wossnig, Leonard Quantum machine learning: a classical perspective |
title | Quantum machine learning: a classical perspective |
title_full | Quantum machine learning: a classical perspective |
title_fullStr | Quantum machine learning: a classical perspective |
title_full_unstemmed | Quantum machine learning: a classical perspective |
title_short | Quantum machine learning: a classical perspective |
title_sort | quantum machine learning: a classical perspective |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806018/ https://www.ncbi.nlm.nih.gov/pubmed/29434508 http://dx.doi.org/10.1098/rspa.2017.0551 |
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