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Grant support

The authors gratefully acknowledges the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Fisica Corpuscular, IFIC (CSIC-UV). This work has been supported by Spanish Project PGC2018-094792-B-100 (MCIU/AEI/FEDER, EU), CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM), and CSIC Platform PTI-001.

Analysis of institutional authors

Altares-Lopez, SAuthorRibeiro, AAuthor

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September 6, 2021
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Automatic design of quantum feature maps

Publicated to:Quantum Science And Technology. 6 (4): 45015- - 2021-10-01 6(4), DOI: 10.1088/2058-9565/ac1ab1

Authors: Altares-Lopez, Sergio; Ribeiro, Angela; Garcia-Ripoll, Juan Jose;

Affiliations

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

Abstract

We propose a new technique for the automatic generation of optimal ad-hoc ansatze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

Keywords

Application fieldsArtificial intelligenceAutomatic designAutomatic generationAutomatic quantum classifier generationGenetic algorithmsMulti-objective genetic algorithmNon linearNon-sorted genetic algorithmsOptimizationQuantum computingQuantum featuresQuantum machine learningQuantum machinesSupport vector machines

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Quantum Science And Technology 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, 2021, it was in position 14/86, thus managing to position itself as a Q1 (Primer Cuartil), in the category Physics, Multidisciplinary.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.64, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 15.5 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-17, the following number of citations:

  • WoS: 3
  • Scopus: 28

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 2025-07-17:

  • 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: 40.
  • 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: 51 (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: 5.5.
  • The number of mentions on the social network X (formerly Twitter): 9 (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) .