Fei Yan

4.7k citations
60 papers · 2.9k indexed · 3 hit papers · h-index 27

Fei Yan

57 papers receiving 2.9k citations

Hit Papers

Beating the bre...1112011202620162021100200300400500

Peers

Fei Yan
Comparison fields: 5 of 96
  • Atomic and Molecular Physics, and Optics 2.1k
  • Artificial Intelligence 1.9k
  • Condensed Matter Physics 192
  • Statistical and Nonlinear Physics 151
  • Polymers and Plastics 146
Replace Dohun Kim with:
Dohun Kim South Korea
Massimo Macucci Italy
Liu Liu China
Linbo Shao United States
Weiping Zhang China
M. Oestreich Germany
Yu He China
Graham T. Reed United Kingdom
Yaoyu Cao China
Takuro Fujii Japan
Fei Yan relative to Dohun Kim South Korea Dohun Kim's profile →
Citations per field
00.5×6.3×
Dohun Kim · 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-authorship network

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
#Work
1 20244
2 20241
3 20248
4 20245
5 20246
6 202323
7 202333
8 20233
9 20236
10
Beating the break-even point with a discrete-variable-encoded logical qubitbreakdown →
2023111
11 202243
12 202236
13 201961
14 2018121
15 201845
16 2018221
17
Gate-tunable Transmon Qubit made with Graphene/hBN Heterostructures
20181
18 201855
19
The Flux Qubit Revisited
20154
20
Z-Gate Operation on a Superconducting Flux Qubit via Its Readout SQUID
20151

About Fei Yan

Fei Yan is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence, Condensed Matter Physics, Biomaterials and Polymers and Plastics, having authored 60 papers that have together received 2.9k indexed citations. Recurring topics across this work include Quantum Information and Cryptography (34 papers), Quantum and electron transport phenomena (29 papers), Quantum Computing Algorithms and Architecture (23 papers), Polymer composites and self-healing (7 papers), Physics of Superconductivity and Magnetism (7 papers), Fiber-reinforced polymer composites (6 papers), Graphene research and applications (5 papers) and biodegradable polymer synthesis and properties (5 papers). The work is most often cited by research in Atomic and Molecular Physics, and Optics (2.1k citations), Artificial Intelligence (1.9k citations), Condensed Matter Physics (192 citations), Statistical and Nonlinear Physics (151 citations) and Polymers and Plastics (146 citations). Fei Yan has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Simon Gustavsson, William D. Oliver, Terry P. Orlando, Fumiki Yoshihara, Jonas Bylander, Yasunobu Nakamura, David G. Cory, K. Harrabi, George Fitch and Jaw Shen Tsai. Their work appears in journals such as Physical Review Letters, Physical Review Applied, Composites Science and Technology, Nature Communications and Physical Review B.

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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