Jiangwei Shang
- Artificial Intelligence top 2%
- Quantum Information and Cryptography 32
- Quantum Computing Algorithms and Architecture 28
- Neural Networks and Reservoir Computing 7
- Gaussian Processes and Bayesian Inference 1
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- Quantum Mechanics and Applications 22
- Atomic and Subatomic Physics Research 1
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- Advanced Thermodynamics and Statistical Mechanics 1
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- Markov Chains and Monte Carlo Methods 2
- Co-authors
- Otfried GühneHui Khoon NgXiao‐Dong YuBerthold‐Georg EnglertHuangjun ZhuZhengyun ZhangTristan KraftRoope Uola
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsStatistical and Nonlinear Physics
In The Last Decade
Jiangwei Shang
31 papers receiving 643 citations
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 591
- Atomic and Molecular Physics, and Optics 510
- Statistical and Nonlinear Physics 62
- Statistics and Probability 13
- Statistics, Probability and Uncertainty 10
Countries citing papers authored by Jiangwei Shang
This map shows the geographic impact of Jiangwei Shang'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 Jiangwei Shang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiangwei Shang more than expected).
Fields of papers citing papers by Jiangwei Shang
This network shows the impact of papers produced by Jiangwei Shang. 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 Jiangwei Shang. The network helps show where Jiangwei Shang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jiangwei Shang, 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 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 6 | |
| 4 | 2023 | 0 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 0 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 14 | |
| 9 | 2021 | 5 | |
| 10 | 2021 | 11 | |
| 11 | 2020 | 22 | |
| 12 | Efficient and practical verification of quantum processes | 2019 | 2 |
| 13 | 2019 | 95 | |
| 14 | 2018 | 16 | |
| 15 | 2018 | 24 | |
| 16 | 2018 | 61 | |
| 17 | 2018 | 47 | |
| 18 | Deterministic realization of superefficient collective measurements via photonic quantum walks | 2017 | 1 |
| 19 | 2012 | 11 | |
| 20 | 2012 | 9 |
About Jiangwei Shang
Jiangwei Shang is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computer Graphics and Computer-Aided Design, having authored 36 papers that have together received 654 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (32 papers), Quantum Computing Algorithms and Architecture (28 papers), Quantum Mechanics and Applications (22 papers), Neural Networks and Reservoir Computing (7 papers), Markov Chains and Monte Carlo Methods (2 papers), Gaussian Processes and Bayesian Inference (1 paper), Advanced Thermodynamics and Statistical Mechanics (1 paper) and Atomic and Subatomic Physics Research (1 paper). The work is most often cited by research in Artificial Intelligence (591 citations), Atomic and Molecular Physics, and Optics (510 citations) and Statistical and Nonlinear Physics (62 citations). Jiangwei Shang has collaborated with scholars based in China, Germany and Singapore. Frequent co-authors include Otfried Gühne, Hui Khoon Ng, Xiao‐Dong Yu, Berthold‐Georg Englert, Huangjun Zhu, Zhengyun Zhang, Tristan Kraft, Roope Uola, Xiangdong Zhang and Ali Asadian. Their work appears in journals such as Physical Review Letters, Nature Communications 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.