Quantum neural network autoencoder and classifier applied to an industrial case study
Authors: Mangini S., Marruzzo A., Piantanida M., Gerace D., Bajoni D., Macchiavello C.
Autors Affiliation: Dipartimento di Fisica, Universita di Pavia, Via Bassi 6, Pavia, I-27100, Italy; INFN Sezione di Pavia, Via Bassi 6, Pavia, I-27100, Italy; Eni SpA, via Emilia 1, San Donato Milanese, I-20097, Italy; Dipartimento di Ingegneria Industriale e dell?Informazione, Universitb di Pavia, Via Ferrata 1, Pavia, 27100, Italy; CNR-INO – Largo, E. Fermi 6, Firenze, I-50125, 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.
Journal/Review: QUANTUM MACHINE INTELLIGENCE
Volume: 4 (2) Pages from: 13-1 to: 13-13
More Information: 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)”.KeyWords: Quantum machine learning; Industrial case study; Quantum autoencoder; Classification; Quantum data analysisDOI: 10.1007/s42484-022-00070-4