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

Anno: 2022

Autori: Mangini S., Marruzzo A., Piantanida M., Gerace D., Bajoni D., Macchiavello C.

Affiliazione autori: Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy; Ist Nazl Fis Nucl, Sez Pavia, Via Bassi 6,1, I-27100 Pavia, Italy; Eni SpA, Via Emilia 1, I-20097 San Donato Milanese, Italy; Univ Pavia, Dipartimento Ingn Ind & Informaz, Via Ferrata 1, I-27100 Pavia, Italy; CNR INO, Largo E Fermi 6, I-50125 Florence, Italy.

Abstract: 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.

Giornale/Rivista: QUANTUM MACHINE INTELLIGENCE

Volume: 4 (2)      Da Pagina: 13-1  A: 13-13

Maggiori informazioni: Open access funding provided by Universita degli Studi di Pavia within the CRUI-CARE Agreement. This work was supported by Eni S.p.A. through the research program “Research, Development and Analysis Activities supporting the Innovation” under Contract No. 2500034935, and by the Italian Ministry of Education, University and Research (MIUR) through the ” Dipartimenti di Eccellenza Program (2018-2022)”.
Parole chiavi: Quantum machine learning; Industrial case study; Quantum autoencoder; Classification; Quantum data analysis
DOI: 10.1007/s42484-022-00070-4

Citazioni: 9
dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2025-02-09
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