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


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.

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  1. 1.

    It should be stressed that quantum-like algorithms implemented by the NQiML approach are not a mere translation of classical algorithms. For instance, in Sergioli et al. (2018b, Section 2.1), the author introduces a quantum-like version of the NMC, which is very different from a mere quantum-theoretic translation of the NMC.

  2. 2.

    For instance, a very usual pre-processing consists in the normalisation of all the vectors of the dataset.


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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.

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Correspondence to Giuseppe Sergioli.

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Sergioli, G. Quantum and quantum-like machine learning: a note on differences and similarities. Soft Comput 24, 10247–10255 (2020). https://doi.org/10.1007/s00500-019-04429-x

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  • Quantum machine learning
  • Quantum information
  • Binary classification