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• W3045879969 abstract "Artificial intelligence algorithms, developed for traditional computing, based on Von Neumann’s architecture, are slow and expensive 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 properties and on the design of their quantum computing versions. More specifically, 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." @default.
• W3045879969 created "2020-08-03" @default.
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• W3045879969 date "2020-07-29" @default.
• W3045879969 modified "2023-10-17" @default.
• W3045879969 title "A Review of k-NN Algorithm Based on Classical and Quantum Machine Learning" @default.
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• W3045879969 doi "https://doi.org/10.1007/978-3-030-53829-3_20" @default.
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