Published March 30, 2022 | Version v1
Conference paper Open

Quantum Deep Learning for Steel Industry Computer Vision Quality Control.

Creators

  • 1. Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
  • 2. Escuela Técnica Superior de Ingenieros Industrials (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
  • 3. Department of Mechanical Engineering, Universidad de La Rioja, San José de Calasanz 31, 26004 Logroño, Spain
  • 4. Sidenor Investigación y Desarrollo SA, Barrio Ugarte s/n, 48970, Basauri, Bizkaia, Spain

Description

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.

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