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This publication is part of the following projects: (1) I+D+i: PID2020-113229RBC43, (2) Proof of Concept PDC2021-121537-C21, both funded by MCIN/AEI/10.13039/501100011033/and (3) Proyecto Misiones IA MIA.2021.M01.0004, funded by the Ministerio de Asuntos Economicos y Transformacion Digital. "Union Europea NextGeneration EU/PRTR", Spain.

Analysis of institutional authors

Altares-López, SergioAuthorRibeiro, AngelaCorresponding Author
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Article

Optimal quantum circuit generation for pixel segmentation in multiband images

Publicated to:Applied Soft Computing. 166 112175- - 2024-11-01 166(), DOI: 10.1016/j.asoc.2024.112175

Authors: Altares-López, S; García-Ripoll, JJ; Ribeiro, A

Affiliations

CSIC, Consejo Super Invest Cient, Inst Fis Fundamental IFF, Calle Serrano 113b, Madrid 28006, Spain - Author
Univ Politecn Madrid, Programa Doctorado Automat & Robot, Calle Jose Gutierrez Abascal 2, Madrid 28006, Spain - Author
UPM, Consejo Super Invest Cient, Ctr Automat & Robot CAR CS, Ctra Campo Real km 0,200, Arganda Del Rey 28500, Spain - Author

Abstract

A novel approach is proposed for multiband image processing via quantum models in real situations. Quantum circuits are automatically generated ad-hoc for each use case via multiobjective genetic algorithms. Using this universal method, image processing tasks such as segmentation can be carried out by considering the properties that constitute each pixel. The generated circuits present a low level of correlation between qubits, and thus can be considered quantum-inspired machine learning models. The effectiveness of this methodology has been validated by applying it to different segmentation use cases. Comparisons are made between optimized classical kernel methods and the generated quantum-inspired models to understand their behaviors. The results show that quantum models for multiband image processing achieve accuracies similar to those of classical methods.

Keywords
ExtractionGenetic algorithmIndexesModeMultiobjective optimizationPixel segmentationQuantum architecture searcQuantum architecture searchQuantum image processingQuantum machine learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Soft Computing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 27/197, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 18.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 18 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.5.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ALTARES LOPEZ, SERGIO) and Last Author (RIBEIRO SEIJAS, ANGELA).

the author responsible for correspondence tasks has been RIBEIRO SEIJAS, ANGELA.