Paper Info
Title
Quantum and quantum-like machine learning: a note on differences and similarities
Abstract
In the past few decades, researchers have extensively investigated the applications of quantum computation and quantum information to machine learning with remarkable results. This, in turn, has led to the emergence of quantum machine learning as a separate discipline, whose main goal is to transform standard machine learning algorithms into quantum algorithms which can be implemented on quantum computers. One further research programme has involved using quantum information to create new quantum-like algorithms for classical computers (Sergioli et al. in Int J Theor Phys 56(12):3880–3888, 2017; PLoS ONE 14:e0216224, 2019. https://doi.org/10.1371/journal.pone.0216224; Int J Quantum Inf 16(8):1840011, 2018a; Soft Comput 22(3):691–705, 2018b). This brief survey summarises and compares both approaches and also outlines the main motivations behind them.
Year
DOI
Venue
2020
10.1007/s00500-019-04429-x
Soft Computing
Keywords
DocType
Volume
Quantum machine learning, Quantum information, Binary classification
Journal
24
Issue
ISSN
Citations 
14
1432-7643
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Giuseppe Sergioli12311.03