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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| Vydáno v: | Quantum machine intelligence Ročník 4; číslo 2 |
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Springer International Publishing
01.12.2022
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| ISSN: | 2524-4906, 2524-4914 |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 13 |
| Author | Mangini, Stefano Piantanida, Marco Marruzzo, Alessia Gerace, Dario Bajoni, Daniele Macchiavello, Chiara |
| Author_xml | – sequence: 1 givenname: Stefano orcidid: 0000-0002-0056-0660 surname: Mangini fullname: Mangini, Stefano email: stefano.mangini01@universitadipavia.it organization: Dipartimento di Fisica, Università di Pavia, INFN Sezione di Pavia – sequence: 2 givenname: Alessia surname: Marruzzo fullname: Marruzzo, Alessia organization: Eni SpA – sequence: 3 givenname: Marco surname: Piantanida fullname: Piantanida, Marco organization: Eni SpA – sequence: 4 givenname: Dario surname: Gerace fullname: Gerace, Dario organization: Dipartimento di Fisica, Università di Pavia – sequence: 5 givenname: Daniele surname: Bajoni fullname: Bajoni, Daniele organization: Dipartimento di Ingegneria Industriale e dell’Informazione, Universitá di Pavia – sequence: 6 givenname: Chiara surname: Macchiavello fullname: Macchiavello, Chiara organization: Dipartimento di Fisica, Università di Pavia, INFN Sezione di Pavia, CNR-INO - Largo |
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| Keywords | Quantum data analysis Quantum machine learning Industrial case study Quantum autoencoder Classification |
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4 HY Huang (70_CR26) 2021; 12 70_CR7 70_CR6 J Preskill (70_CR47) 2018; 2 70_CR46 70_CR9 J Romero (70_CR48) 2017; 2 M Schuld (70_CR51) 2021; 103 AM Childs (70_CR15) 2020; 375 S Mangini (70_CR37) 2021; 134 PK Barkoutsos (70_CR4) 2018; 98 A Peruzzo (70_CR45) 2014; 5 |
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