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Effectiveness of Quantum Computing in Image Processing for Burr Detection

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

  • 170 Accesses

Abstract

Technology is developing at a dizzying pace to make our lives easier. Among other innovations, quantum computing, which is based on quantum mechanics, is evolving with the aim of solving all the problems that traditional computing is unable to solve. Quantum computing, unlike traditional computing, relies on qubits (quantum bits) that can be in superposition of the values 0 and 1, as well as other fundamentals, such as interference or entanglement. This paper analyses the use of quantum computing for image processing, testing the efficiency when quantum computers are employed. To do that, the Flexible Representation of Quantum Images model of image representation is implemented in a quantum simulator to solve a particular problem. Therefore, images of machined parts are processed in order to detect the edges of the mask what can determine the presence of burrs. Detecting burrs improves the surface finish and, in turn, the quality of industrial processes. The results of the proof of concept show that, although compared to traditional computing, quantum algorithms are not yet efficient enough to match or even surpass classical algorithms, they are promising.

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Acknowledgements

We gratefully acknowledge the financial support of Spanish Ministry of Economy and Competitiveness (grant PID2019-108277GB-C21). This work has been financially supported by the Ministry of Economic Affairs and Digital Transformation of the Spanish Government through the QUANTUM ENIA project call - Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan - NextGenerationEU within the framework of the Digital Spain 2026 Agenda. We also express our grateful to Centro de Supercomputación de Castilla y León (SCAYLE) for its infrastructure support.

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Authors and Affiliations

  1. SCAYLE: Fundación Centro de Supercomputación de Castilla y León, León, Spain

    Santiago Merino-Bajo & Vicente Matellán

  2. Departamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, 24071, León, Spain

    Lidia Sánchez-González & Virginia Riego

Authors
  1. Santiago Merino-Bajo
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  2. Lidia Sánchez-González
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  3. Virginia Riego
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  4. Vicente Matellán
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Corresponding author

Correspondence to Lidia Sánchez-González .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Merino-Bajo, S., Sánchez-González, L., Riego, V., Matellán, V. (2023). Effectiveness of Quantum Computing in Image Processing for Burr Detection. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_10

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