Artículo
Quantum Machine Learning: A tutorial
Autor/es | Martín Guerrero, José D.
Lamata Manuel, Lucas |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Fecha de publicación | 2022 |
Fecha de depósito | 2021-12-22 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Ciencia, Innovación y Universidades (MICINN). España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) |
Identificador del proyecto | GC2018-095113-B-I00
PID2019-104002GB-C21 PID2019-104002GB-C22 |
Cita | Martín Guerrero, J.D. y Lamata Manuel, L. (2022). Quantum Machine Learning: A tutorial. Neurocomputing, 470, 457-461. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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1-s2.0-S0925231221011000-main.pdf | 361.3Kb | [PDF] | Ver/ | |
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