Fei Yan

2.3k citations
81 papers · 1.7k · h-index 23

Impact in

Papers in

Fei Yan

76 papers receiving 1.6k citations

Peers

Fei Yan
Comparison fields: 5 of 100
  • Artificial Intelligence 1.4k
  • Computational Theory and Mathematics 484
  • Acoustics and Ultrasonics 19
  • Computer Vision and Pattern Recognition 393
  • Atomic and Molecular Physics, and Optics 314
Replace Salvador E. Venegas-Andraca with:
Salvador E. Venegas-Andraca Mexico
Nan Jiang China
Fangyan Dong Japan
WenWen Hu China
Ping Fan China
Phuc Q. Le Japan
Vedran Dunjko Netherlands
Li‐Hua Gong China
Osama S. Faragallah Egypt
Fei Yan relative to Salvador E. Venegas-Andraca Mexico Salvador E. Venegas-Andraca's profile →
Citations per field
00.5×
Salvador E. Venegas-Andraca · 1×
Citations per year

Countries citing papers authored by Fei Yan

Since Specialization
Citations

This map shows the geographic impact of Fei Yan'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 Fei Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Yan more than expected).

Fields of papers citing papers by Fei Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fei Yan. 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 Fei Yan. The network helps show where Fei Yan may publish in the future.

Co-authors

The 25 scholars most cited alongside Fei Yan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fei Yan Line = papers co-authored together Fei Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015227
2 2013158
3 2017116
4 201195
5 201761
6 201552
7 201449
8 202346
9 202046
10 202139
11 202238
12 201237
13 202136
14 201335
15 202133
16 201533
17 202330
18
Quantum secure direct communication by EPR pairs and entanglement swapping
200430
19 201729
20 201623

About Fei Yan

Fei Yan is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering, having authored 81 papers that have together received 1.7k indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (51 papers), Quantum Information and Cryptography (42 papers), Quantum-Dot Cellular Automata (14 papers), Quantum Mechanics and Applications (10 papers), Neural Networks and Reservoir Computing (8 papers), Chaos-based Image/Signal Encryption (6 papers), Computability, Logic, AI Algorithms (6 papers) and Emotion and Mood Recognition (5 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Computational Theory and Mathematics (484 citations), Acoustics and Ultrasonics (19 citations), Computer Vision and Pattern Recognition (393 citations) and Atomic and Molecular Physics, and Optics (314 citations). Fei Yan has collaborated with scholars based in China, Japan and Saudi Arabia. Frequent co-authors include Abdullah M. Iliyasu, Kaoru Hirota, Salvador E. Venegas-Andraca, Fangyan Dong, Bo Sun, Phuc Q. Le, Kaoru Hirota, Huamin Yang, Zhengang Jiang and Witold Pedrycz. Their work appears in journals such as Quantum Information Processing, Physical Review Letters, Information Sciences, Scientific Reports and Electronics.

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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