Quantum Deep Learning for Steel Industry Computer Vision Quality Control

被引：5

作者：

Villalba-Diez, Javier ^{[1
,2
]}

Ordieres-Mere, Joaquin ^{[3
]}

Gonzalez-Marcos, Ana ^{[4
]}

Larzabal, Aintzane Soto ^{[5
]}

机构：

[1] Hsch Heilbronn, Fak Management & Vertrieb, Campus Schwabisch Hall, D-74523 Schwabisch Hall, Germany

[2] Univ Politecn Madrid, Complex Syst Grp, Av Puerta Hierro 2, Madrid 28040, Spain

[3] Univ Politecn Madrid, Escuela Tecn Super Ingn Ind ETSII, Jose Gutierrez Abascal 2, Madrid 28006, Spain

[4] Univ La Rioja, Dept Mech Engn, San Jose Calasanz 31, Logrono 26004, Spain

[5] Sidenor Invest & Desarrollo SA, Barrio Ugarte S-N, Basauri 48970, Bizkaia, Spain

来源：

IFAC PAPERSONLINE | 2022年 / 55卷 / 02期

关键词：

Steel Descaler; Quality of Steel Billet; Quantum Deep Learning; Quantvolutional Neural Network; Deep Learning; Quality in Steel Industry; DEFECTS; CARBON;

D O I：

10.1016/j.ifacol.2022.04.216

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The aim of this paper is to explore the potential capabilities of quantum machine learning technology (a branch of quantum computing) when applied to surface quality supervision inside steel manufacturing processes where environmental conditions can affect the quality of images. Comparison with classical deep learning classification schema is performed. The application case, driven by the so-called quantvolutional configuration, shows a large potential of using this technology in this field, mainly because of the speed when using a physical quantum engine.

引用

页码：337 / 342

页数：6

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