Quantum and quantum-like machine learning: a note on differences and similarities

  • Giuseppe SergioliEmail author


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.; 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.


Quantum machine learning Quantum information Binary classification 



I warmly thank Claudio Ternullo for the careful linguistic revision of the last version of the manuscript.


This work has been partially supported by the Project “Strategies and Technologies for Scientific Education and Dissemination” (CUP No. F71I17000330002) founded by Fondazione di Sardegna.

Compliance with ethical standards

Conflict of interest

The author does not have any conflicts of interest.

Ethical standard

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of CagliariCagliariItaly

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