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Analysis of institutional authors

Domingo, LCorresponding Author

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November 7, 2022
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Optimal quantum reservoir computing for the noisy intermediate-scale quantum era

Publicated to: Physical Review e. 106 (4): L043301- - 2022-01-01 106(4), DOI: 10.1103/PhysRevE.106.L043301

Authors:

Domingo, L; Carlo, G; Borondo, F
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Affiliations

Consejo Nacl Invest Cient & Tecn, Comis Nacl Energia Atom, Dept Fis, Ave Libertador 8250, RA-1429 Buenos Aires, Argentina - Author
Inst Ciencias Matemat, Campus Cantoblanco 13-15, Madrid 28049, Spain - Author
Univ Autonoma Madrid, Dept Quim, Madrid 28049, Spain - Author
Univ Politecn Madrid, Grp Sistemas Complejos, Madrid 28035, Spain - Author
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Abstract

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this tech-nology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today's available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states' complexity.
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Keywords

Classical simulation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Physical Review e 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, 2022, it was in position 11/56, thus managing to position itself as a Q1 (Primer Cuartil), in the category Physics, Mathematical.

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: 1.43, 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 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-12-21, the following number of citations:

  • WoS: 1
  • Scopus: 8
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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 2025-12-21:

  • 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: 8.
  • 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: 10 (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: 24.
  • The number of mentions in news outlets: 3 (Altmetric).
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Argentina.

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 (DOMINGO COLOMER, LAIA) .

the author responsible for correspondence tasks has been DOMINGO COLOMER, LAIA.

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Awards linked to the item

The project that gave rise to these results received the support of a fellowship from "la Caixa" Foundation (ID No. 100010434). The fellowship code is LCF/BQ/DR20/11790028. This work has also been partially supported by the Spanish Ministry of Science, Innovation, and Universities, Gobierno de Espana, under Contracts No. PGC2018-093854-BI00 and No. ICMAT Severo Ochoa CEX2019-000904-S and by the People Programme (Marie Curie Actions) of the European Union's Horizon 2020 Research and Innovation Program under Grant No. 734557.
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