Carlos Bravo-Prieto

673 total citations
10 papers, 318 citations indexed

About

Carlos Bravo-Prieto is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Hardware and Architecture. According to data from OpenAlex, Carlos Bravo-Prieto has authored 10 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Atomic and Molecular Physics, and Optics and 1 paper in Hardware and Architecture. Recurrent topics in Carlos Bravo-Prieto's work include Quantum Computing Algorithms and Architecture (10 papers), Quantum Information and Cryptography (8 papers) and Quantum Mechanics and Applications (3 papers). Carlos Bravo-Prieto is often cited by papers focused on Quantum Computing Algorithms and Architecture (10 papers), Quantum Information and Cryptography (8 papers) and Quantum Mechanics and Applications (3 papers). Carlos Bravo-Prieto collaborates with scholars based in Spain, Singapore and United Arab Emirates. Carlos Bravo-Prieto's co-authors include José I. Latorre, Diego García-Martín, M. Cerezo, Ryan LaRose, Łukasz Cincio, Patrick J. Coles, Yiğit Subaşı, Stefano Carrazza, Elies Gil-Fuster and Jens Eisert and has published in prestigious journals such as Nature Communications, Physical review. A and Journal of Physics A Mathematical and Theoretical.

In The Last Decade

Carlos Bravo-Prieto

10 papers receiving 309 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Carlos Bravo-Prieto Spain	9	284	99	66	27	14	10	318
Diego García-Martín Spain	9	284 1.0×	130 1.3×	47 0.7×	22 0.8×	5 0.4×	14	326
Natalie C. Brown United States	8	301 1.1×	168 1.7×	65 1.0×	50 1.9×	4 0.3×	13	336
Richard Rines United States	4	252 0.9×	174 1.8×	46 0.7×	29 1.1×	3 0.2×	4	318
Patrick Rall United States	9	322 1.1×	192 1.9×	82 1.2×	35 1.3×	7 0.5×	13	383
Leo Zhou United States	6	208 0.7×	81 0.8×	70 1.1×	29 1.1×	6 0.4×	9	235
Aniruddha Bapat United States	6	237 0.8×	132 1.3×	64 1.0×	11 0.4×	8 0.6×	11	275
Sofiène Jerbi Austria	6	255 0.9×	71 0.7×	34 0.5×	35 1.3×	3 0.2×	11	289
Bibek Pokharel United States	7	264 0.9×	195 2.0×	23 0.3×	28 1.0×	7 0.5×	14	318
Changpeng Shao United Kingdom	9	204 0.7×	71 0.7×	60 0.9×	19 0.7×	4 0.3×	24	242
Guillaume Verdon Canada	4	340 1.2×	114 1.2×	61 0.9×	47 1.7×	2 0.1×	5	411

Countries citing papers authored by Carlos Bravo-Prieto

Since Specialization

Citations

This map shows the geographic impact of Carlos Bravo-Prieto's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Carlos Bravo-Prieto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlos Bravo-Prieto more than expected).

Fields of papers citing papers by Carlos Bravo-Prieto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carlos Bravo-Prieto. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Carlos Bravo-Prieto. The network helps show where Carlos Bravo-Prieto may publish in the future.

Co-authorship network of co-authors of Carlos Bravo-Prieto

This figure shows the co-authorship network connecting the top 25 collaborators of Carlos Bravo-Prieto. A scholar is included among the top collaborators of Carlos Bravo-Prieto based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Carlos Bravo-Prieto. Carlos Bravo-Prieto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Gil-Fuster, Elies, Jens Eisert, & Carlos Bravo-Prieto. (2024). Understanding quantum machine learning also requires rethinking generalization. Nature Communications. 15(1). 2277–2277. 28 indexed citations

2.

Bravo-Prieto, Carlos, Ryan LaRose, M. Cerezo, et al.. (2023). Variational Quantum Linear Solver. Quantum. 7. 1188–1188. 84 indexed citations

3.

Bravo-Prieto, Carlos, et al.. (2022). Variational quantum eigensolver for SU( N ) fermions. Journal of Physics A Mathematical and Theoretical. 55(26). 265301–265301. 9 indexed citations

4.

Bravo-Prieto, Carlos, Julien Baglio, Marco Cè, et al.. (2022). Style-based quantum generative adversarial networks for Monte Carlo events. Quantum. 6. 777–777. 27 indexed citations

5.

Bravo-Prieto, Carlos, et al.. (2022). Solving systems of Boolean multivariate equations with quantum annealing. Physical Review Research. 4(1). 2 indexed citations

6.

Efthymiou, Stavros, Carlos Bravo-Prieto, Diego García-Martín, et al.. (2021). Qibo : a framework for quantum simulation with hardware acceleration. Quantum Science and Technology. 7(1). 15018–15018. 72 indexed citations

7.

García-Martín, Diego, et al.. (2021). Quantum unary approach to option pricing. Physical review. A. 103(3). 27 indexed citations

8.

García-Martín, Diego, et al.. (2020). Measuring the Tangle of Three-Qubit States. Entropy. 22(4). 436–436. 9 indexed citations

9.

Bravo-Prieto, Carlos, Diego García-Martín, & José I. Latorre. (2020). Quantum singular value decomposer. Physical review. A. 101(6). 44 indexed citations

10.

Bravo-Prieto, Carlos, Ryan LaRose, M. Cerezo, et al.. (2019). Variational Quantum Linear Solver: A Hybrid Algorithm for Linear Systems. arXiv (Cornell University). 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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