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Quantum Machine Learning

Authors: Romero García, Cristian;

Quantum Machine Learning

Abstract

[ES] En un mundo en el que el acceso a la información crece exponencialmente, la selección de la información apropiada resulta ser un problema muy importante. En este contexto, surge la idea del Machine Learning (ML), un área de la Inteligencia Artificial cuyo fin es afrontar diversos problemas en minería de datos, reconocimiento de patrones, predicción automática, entre otras cosas. Quantum Machine Learning es un área de investigación interdisciplinar que combina la Mecánica Cuántica con métodos de Machine Learning, de forma que las propiedades cuánticas permitan mejorar la eficiencia de los distintos algoritmos. Actualmente, este campo es una de las principales áreas de investigación en diversas compañías como Google y Microsoft, dada la ventaja que puede suponer de cara al tratamiento de datos.

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow for an exponential speed-up in the algorithms. This field is currently one of the main research areas in companies like Google and Microsoft, due to the revolution that they could provide in data management.

Country
Spain
Keywords

machine learning, algorithm, algoritmo, quantum machine learning, computación cuántica, quantum computing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green