A review of k-NN algorithm based on classical and Quantum Machine Learning

Autor:	Mezquita Martín, Yeray, Alonso Rincón, Ricardo Serafín, Casado Vara, Roberto Carlos, Prieto Tejedor, Javier, Corchado Rodríguez, Juan Manuel
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	1203.04 Inteligencia Artificial Machine learning 5306.02 Innovación Tecnológica Quantum computing k-Nearest Neighbors Supervised learning Quantum k-NN
Zdroj:	GREDOS. Repositorio Institucional de la Universidad de Salamanca instname
Popis:	[EN] Artificial intelligence algorithms, developed for traditional computing, based on Von Neumann’s architecture, are slow and expen- sive in terms of computational resources. Quantum mechanics has opened up a new world of possibilities within this field, since, thanks to the basic properties of a quantum computer, a great degree of parallelism can be achieved in the execution of the quantum version of machine learning algorithms. In this paper, a study has been carried out on these proper- ties and on the design of their quantum computing versions. More specif- ically, the study has been focused on the quantum version of the k-NN algorithm that allows to understand the fundamentals when transcribing classical machine learning algorithms into its quantum versions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::55d76c0cb6928c33194b0fd0e8805611 https://hdl.handle.net/10366/149863 Zobrazit plný text záznamu