Xiaoguang Wang
- Artificial Intelligence top 0.5%
- Quantum Information and Cryptography 45
- Quantum Computing Algorithms and Architecture 23
- Bayesian Methods and Mixture Models 10
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- Quantum Mechanics and Applications 29
- Statistics and Probability top 2%
- Statistical Methods and Inference 34
- Statistical Methods and Bayesian Inference 20
- Advanced Statistical Methods and Models 12
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- Simulation Techniques and Applications 17
- Co-authors
- Xiao-Ming LuC. P. SunJian MaZhengjun XiKlaus MølmerAnders S. SørensenJing LiuStephen John Turner
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsStatistical and Nonlinear Physics
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Xiaoguang Wang
178 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 1.7k
- Atomic and Molecular Physics, and Optics 1.6k
- Statistical and Nonlinear Physics 294
- Statistics and Probability 161
- Management Science and Operations Research 188
Countries citing papers authored by Xiaoguang Wang
This map shows the geographic impact of Xiaoguang Wang'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 Xiaoguang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoguang Wang more than expected).
Fields of papers citing papers by Xiaoguang Wang
This network shows the impact of papers produced by Xiaoguang Wang. 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 Xiaoguang Wang. The network helps show where Xiaoguang Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiaoguang Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 4 | |
| 2 | 2024 | 9 | |
| 3 | 2024 | 7 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 7 | |
| 8 | 2023 | 7 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 3 | |
| 11 | 2022 | 5 | |
| 12 | 2022 | 1 | |
| 13 | 2021 | 9 | |
| 14 | 2020 | 2 | |
| 15 | 2020 | 4 | |
| 16 | 2019 | 1 | |
| 17 | 2019 | 0 | |
| 18 | 2005 | 10 | |
| 19 | 2005 | 11 | |
| 20 | 2002 | 29 |
About Xiaoguang Wang
Xiaoguang Wang is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 194 papers that have together received 3.0k indexed citations. Recurring topics across this work include Quantum Information and Cryptography (45 papers), Statistical Methods and Inference (34 papers), Quantum Mechanics and Applications (29 papers), Quantum Computing Algorithms and Architecture (23 papers), Statistical Methods and Bayesian Inference (20 papers), Simulation Techniques and Applications (17 papers), Advanced Statistical Methods and Models (12 papers) and Bayesian Methods and Mixture Models (10 papers). The work is most often cited by research in Artificial Intelligence (1.7k citations), Atomic and Molecular Physics, and Optics (1.6k citations) and Statistical and Nonlinear Physics (294 citations). Xiaoguang Wang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Xiao-Ming Lu, C. P. Sun, Jian Ma, Zhengjun Xi, Klaus Mølmer, Anders S. Sørensen, Jing Liu, Stephen John Turner, Lixin Song and Adam Miranowicz. Their work appears in journals such as Physical Review Letters, Biometrics and Physical Review A.
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.