{rfName}
QF

License and Use

Icono OpenAccess

Altmetrics

Analysis of institutional authors

Martin-Delgado, MaAuthor

Share

March 28, 2022
Publications
>
Article

QFold: quantum walks and deep learning to solve protein folding

Publicated to: Quantum Science and Technology. 7 (2): 25013- - 2022-04-01 7(2), DOI: 10.1088/2058-9565/ac4f2f

Authors:

Casares, PAM; Campos, R; Martin-Delgado, MA
[+]

Affiliations

CENTRO INVEST. SIMULACIÓN COMPUTACIONAL. Universidad Politécnica de Madrid - Author
Quasar Sci Resources SL, Madrid, Spain - Author
Univ Complutense Madrid, Dept Fis Teor, Madrid, Spain - Author
Univ Politecn Madrid, CCS Ctr Computat Simulat, Madrid, Spain - Author
Universidad Politécnica de Madrid - Author
See more

Abstract

We develop quantum computational tools to predict the 3D structure of proteins, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in IBMQ Casablanca quantum system.
[+]

Keywords

Deep learningProtein foldingQuantum advantageQuantum metropolisQuantum simulationQuantum walksStructure prediction

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, 2022, it was in position 13/85, 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 provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.41. This 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)

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:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.9 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-06, the following number of citations:

  • WoS: 21
  • Scopus: 24
[+]

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 2026-04-06:

  • 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: 67.
  • 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: 67 (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: 26.
  • The number of mentions on the social network X (formerly Twitter): 33 (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: Last Author (MARTÍN-DELGADO ALCÁNTARA, MIGUEL ÁNGEL).

[+]

Awards linked to the item

PAMC and RC contributed equally to this work. We would like to thank kind advice from Jaime Sevilla on von Mises distribution and statistical t-tests, Alvaro Martinez del Pozo and Antonio Rey on protein folding, Andrew W Senior on minor details of his AlphaFold article, Carmen Recio, Juan Gomez, Juan Cruz Benito, Kevin Krsulich and Maddy Todd on the usage of Qiskit, and Jessica Lemieux and the late David Poulin on aspects of the quantum Metropolis algorithm. We acknowledge the access to advanced services provided by the IBM Quantum Researchers Program. We also thank Quasar Science for facilitating the access to the AWS resources. We acknowledge financial support from the Spanish MINECO Grants MINECO/FEDER Projects FIS 2017-91460-EXP, PGC2018-099169-B-I00 FIS-2018 and from CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM). The research of MAM-D has been partially supported by the U.S. Army Research Office through Grant No. W911NF-14-1-0103. PAMC thanks the support of a MECD Grant FPU17/03620, and RC the support of a CAM Grant IND2019/TIC17146.
[+]