New trends in quantum machine learning

Year: 2020

Authors: Buffoni L., Caruso F.

Autors Affiliation: Univ Firenze, Dipartimento Fis & Astron, I-50019 Sesto Fiorentino, Italy; Univ Firenze, Dipartimento Ingn Informaz, I-50199 Florence, Italy; QSTAR, LENS, I-50019 Sesto Fiorentino, Italy; CNR INO, I-50019 Sesto Fiorentino, Italy.

Abstract: Here we will give a perspective on new possible interplays between machine learning and quantum physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as quantum machine learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines. Copyright (C) 2021 EPLA

Journal/Review: EUROPHYSICS LETTERS

Volume: 132 (6)      Pages from: 60004-1  to: 60004-7

More Information: The work was financially supported from by the Fondazione CR Firenze through the project QUANTUM-AI, the European Union’s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS), and from University of Florence through the project Q-CODYCES.
KeyWords: Algorithm
DOI: 10.1209/0295-5075/132/60004

Citations: 14
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