Yu-Chun Wu
- Artificial Intelligence top 2%
- Quantum Computing Algorithms and Architecture 46
- Quantum Information and Cryptography 46
- Neural Networks and Reservoir Computing 7
-
- Quantum Mechanics and Applications 25
- Quantum many-body systems 7
- Quantum and electron transport phenomena 4
-
- Quantum-Dot Cellular Automata 6
- Polynomial and algebraic computation 3
- Co-authors
- Guang‐Can GuoGuo‐Ping GuoCheng XueZhaoyun ChenZhen−Qiang YinZheng‐Fu HanHong-Wei LiXu‐Bo Zou
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsComputational Theory and Mathematics
- Journals
- Physical Review Letters (1 paper)Nature Communications (1 paper)The Journal of Chemical Physics (2 papers)
- Partner nations
- ChinaPolandUnited States
In The Last Decade
Yu-Chun Wu
61 papers receiving 603 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 494
- Atomic and Molecular Physics, and Optics 362
- Computational Theory and Mathematics 74
- Statistical and Nonlinear Physics 39
- Ecological Modeling 12
Countries citing papers authored by Yu-Chun Wu
This map shows the geographic impact of Yu-Chun Wu'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 Yu-Chun Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu-Chun Wu more than expected).
Fields of papers citing papers by Yu-Chun Wu
This network shows the impact of papers produced by Yu-Chun Wu. 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 Yu-Chun Wu. The network helps show where Yu-Chun Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yu-Chun Wu, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 0 | |
| 6 | 2024 | 10 | |
| 7 | 2024 | 2 | |
| 8 | 2024 | 4 | |
| 9 | 2024 | 0 | |
| 10 | 2023 | 0 | |
| 11 | 2023 | 1 | |
| 12 | 2023 | 9 | |
| 13 | 2023 | 2 | |
| 14 | 2022 | 4 | |
| 15 | 2022 | 4 | |
| 16 | 2022 | 2 | |
| 17 | 2021 | 0 | |
| 18 | 2020 | 8 | |
| 19 | 2020 | 7 | |
| 20 | Quantum Neural Network States | 2018 | 1 |
About Yu-Chun Wu
Yu-Chun Wu is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 73 papers that have together received 618 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (46 papers), Quantum Information and Cryptography (46 papers), Quantum Mechanics and Applications (25 papers), Quantum many-body systems (7 papers), Neural Networks and Reservoir Computing (7 papers), Quantum-Dot Cellular Automata (6 papers), Quantum and electron transport phenomena (4 papers) and Polynomial and algebraic computation (3 papers). The work is most often cited by research in Artificial Intelligence (494 citations), Atomic and Molecular Physics, and Optics (362 citations) and Computational Theory and Mathematics (74 citations). Yu-Chun Wu has collaborated with scholars based in China, Poland and United States. Frequent co-authors include Guang‐Can Guo, Guo‐Ping Guo, Guang‐Can Guo, Cheng Xue, Zhaoyun Chen, Zhen−Qiang Yin, Zheng‐Fu Han, Hong-Wei Li, Xu‐Bo Zou and Wei Chen. Their work appears in journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.
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.