F. Albarrán-Arriagada
- Artificial Intelligence top 5%
- Atomic and Molecular Physics, and Optics top 10%
- Statistical and Nonlinear Physics top 10%
- Electrical and Electronic Engineering
- Computational Theory and Mathematics top 10%
- Co-authors
- E. SolanoJ. C. RetamalLucas LamataNarendra N. HegadeXi ChenKoushik PaulG. RomeroAdolfo del Campo
- Topics
- Quantum Information and Cryptography (26 papers)Quantum Computing Algorithms and Architecture (20 papers)Neural Networks and Reservoir Computing (10 papers)
In The Last Decade
F. Albarrán-Arriagada
31 papers receiving 368 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 292
- Atomic and Molecular Physics, and Optics 201
- Statistical and Nonlinear Physics 73
- Electrical and Electronic Engineering 45
- Computational Theory and Mathematics 42
Countries citing papers authored by F. Albarrán-Arriagada
This map shows the geographic impact of F. Albarrán-Arriagada'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 F. Albarrán-Arriagada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F. Albarrán-Arriagada more than expected).
Fields of papers citing papers by F. Albarrán-Arriagada
This network shows the impact of papers produced by F. Albarrán-Arriagada. 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 F. Albarrán-Arriagada. The network helps show where F. Albarrán-Arriagada may publish in the future.
Co-authorship network of co-authors of F. Albarrán-Arriagada
This figure shows the co-authorship network connecting the top 25 collaborators of F. Albarrán-Arriagada. A scholar is included among the top collaborators of F. Albarrán-Arriagada 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 F. Albarrán-Arriagada. F. Albarrán-Arriagada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 5 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 12 | |
| 12 | 4 | |
| 13 | 5 | |
| 14 | 2 | |
| 15 | 28 | |
| 16 | 51 | |
| 17 | Reconstruction of a Photonic Qubit State with Quantum Reinforcement Learning | 2 |
| 18 | 12 | |
| 19 | 14 | |
| 20 | 4 |
About F. Albarrán-Arriagada
F. Albarrán-Arriagada is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics, having authored 33 papers that have together received 377 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (26 papers), Quantum Computing Algorithms and Architecture (20 papers) and Neural Networks and Reservoir Computing (10 papers). The work is most often cited by research in Artificial Intelligence (292 citations), Atomic and Molecular Physics, and Optics (201 citations) and Statistical and Nonlinear Physics (73 citations). F. Albarrán-Arriagada has collaborated with scholars based in Chile, China and Spain. Frequent co-authors include E. Solano, J. C. Retamal, Lucas Lamata, Narendra N. Hegade, Xi Chen, Koushik Paul, G. Romero, Adolfo del Campo, Guang‐Can Guo and Mikel Sanz. Their work appears in journals such as Physical Review Letters, Scientific Reports and Europhysics Letters (EPL).
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.