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.