Quantum neural network autoencoder and classifier applied to an industrial case study

Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is relevant to develop algorithms that are useful for ac...

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Bibliographic Details
Published in:Quantum machine intelligence Vol. 4; no. 2
Main Authors: Mangini, Stefano, Marruzzo, Alessia, Piantanida, Marco, Gerace, Dario, Bajoni, Daniele, Macchiavello, Chiara
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2022
Subjects:
Artificial Intelligence
Computational Intelligence
Engineering
Quantum Information Technology
Research Article
Spintronics
Quantum data analysis
Quantum machine learning
Industrial case study
Quantum autoencoder
Classification
ISSN:2524-4906, 2524-4914
Online Access:Get full text
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Summary:Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is relevant to develop algorithms that are useful for actual industrial processes. In this work, we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni’s Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.
ISSN:2524-4906
2524-4914
DOI:10.1007/s42484-022-00070-4