Shi-Ju Ran
Impact in
- Computational Mathematics top 1%
- Tensor decomposition and applications
- Condensed Matter Physics top 5%
- Physics of Superconductivity and Magnetism
- Advanced Condensed Matter Physics
Papers in
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- Quantum many-body systems 37
- Quantum and electron transport phenomena 11
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- Quantum Computing Algorithms and Architecture 15
- Quantum Information and Cryptography 8
- Co-authors
- Gang Su (24 shared papers)Maciej Lewenstein (15 shared papers)Peng Cheng (8 shared papers)Bin Xi (5 shared papers)Wei Li (5 shared papers)A. Bermúdez (1 shared paper)Matteo Rizzi (1 shared paper)Emanuele Tirrito (4 shared papers)
In The Last Decade
Shi-Ju Ran
46 papers receiving 784 citations
Peers
Comparison fields: 5 of 52
- Computational Mathematics 86
- Condensed Matter Physics 286
- Atomic and Molecular Physics, and Optics 595
- Artificial Intelligence 315
- Statistical and Nonlinear Physics 94
Countries citing papers authored by Shi-Ju Ran
This map shows the geographic impact of Shi-Ju Ran'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 Shi-Ju Ran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shi-Ju Ran more than expected).
Fields of papers citing papers by Shi-Ju Ran
This network shows the impact of papers produced by Shi-Ju Ran. 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 Shi-Ju Ran. The network helps show where Shi-Ju Ran may publish in the future.
Co-authors
The 25 scholars most cited alongside Shi-Ju Ran, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 111 | |
| 2 | 2020 | 104 | |
| 3 | 2020 | 83 | |
| 4 | 2011 | 72 | |
| 5 | 2012 | 49 | |
| 6 | 2013 | 31 | |
| 7 | 2020 | 31 | |
| 8 | 2021 | 21 | |
| 9 | 2012 | 21 | |
| 10 | 2018 | 18 | |
| 11 | 2013 | 16 | |
| 12 | 2010 | 16 | |
| 13 | 2017 | 16 | |
| 14 | 2021 | 15 | |
| 15 | 2014 | 15 | |
| 16 | 2021 | 14 | |
| 17 | 2021 | 13 | |
| 18 | 2017 | 13 | |
| 19 | 2019 | 12 | |
| 20 | Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems | 2020 | 12 |
About Shi-Ju Ran
Shi-Ju Ran is a scholar working on Atomic and Molecular Physics, and Optics, Artificial Intelligence, Condensed Matter Physics, Statistical and Nonlinear Physics and Computational Mathematics, having authored 48 papers that have together received 802 indexed citations. Recurring topics across this work include Quantum many-body systems (37 papers), Quantum Computing Algorithms and Architecture (15 papers), Physics of Superconductivity and Magnetism (13 papers), Quantum and electron transport phenomena (11 papers), Advanced Condensed Matter Physics (8 papers), Quantum Information and Cryptography (8 papers), Theoretical and Computational Physics (7 papers) and Tensor decomposition and applications (6 papers). The work is most often cited by research in Computational Mathematics (86 citations), Condensed Matter Physics (286 citations), Atomic and Molecular Physics, and Optics (595 citations), Artificial Intelligence (315 citations) and Statistical and Nonlinear Physics (94 citations). Shi-Ju Ran has collaborated with scholars based in China, Spain and Germany. Frequent co-authors include Gang Su, Maciej Lewenstein, Peng Cheng, Bin Xi, Wei Li, A. Bermúdez, Matteo Rizzi, Emanuele Tirrito, Shou-Shu Gong and Luca Tagliacozzo. Their work appears in journals such as Physical review. B., Physical Review B, Chinese Physics Letters, Physical Review Letters and Physical review. E.
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.