Yong Siah Teo
- Acoustics and Ultrasonics top 10%
- Artificial Intelligence top 5%
- Quantum Information and Cryptography 42
- Quantum Computing Algorithms and Architecture 16
- Neural Networks and Reservoir Computing 4
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- Quantum Mechanics and Applications 22
- Quantum optics and atomic interactions 3
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- Statistical Mechanics and Entropy 7
- Advanced Thermodynamics and Statistical Mechanics 2
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- Sparse and Compressive Sensing Techniques 8
Yong Siah Teo
44 papers receiving 494 citations
Peers
Comparison fields: 5 of 37
- Acoustics and Ultrasonics 19
- Artificial Intelligence 453
- Atomic and Molecular Physics, and Optics 333
- Statistical and Nonlinear Physics 63
- Computational Mechanics 48
Countries citing papers authored by Yong Siah Teo
This map shows the geographic impact of Yong Siah Teo'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 Yong Siah Teo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yong Siah Teo more than expected).
Fields of papers citing papers by Yong Siah Teo
This network shows the impact of papers produced by Yong Siah Teo. 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 Yong Siah Teo. The network helps show where Yong Siah Teo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yong Siah Teo, 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 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 17 | |
| 7 | 2022 | 17 | |
| 8 | Benchmarking quantum tomography completeness and fidelity with machine learning | 2021 | 12 |
| 9 | 2020 | 6 | |
| 10 | 2020 | 16 | |
| 11 | 2020 | 33 | |
| 12 | 2019 | 30 | |
| 13 | 2019 | 3 | |
| 14 | 2018 | 5 | |
| 15 | 2016 | 9 | |
| 16 | 2015 | 1 | |
| 17 | 2014 | 16 | |
| 18 | 2013 | 5 | |
| 19 | 2011 | 81 | |
| 20 | 2010 | 14 |
About Yong Siah Teo
Yong Siah Teo is a scholar working on Acoustics and Ultrasonics, Artificial Intelligence, Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics and Computational Mechanics, having authored 46 papers that have together received 506 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (42 papers), Quantum Mechanics and Applications (22 papers), Quantum Computing Algorithms and Architecture (16 papers), Sparse and Compressive Sensing Techniques (8 papers), Statistical Mechanics and Entropy (7 papers), Neural Networks and Reservoir Computing (4 papers), Quantum optics and atomic interactions (3 papers) and Advanced Thermodynamics and Statistical Mechanics (2 papers). The work is most often cited by research in Acoustics and Ultrasonics (19 citations), Artificial Intelligence (453 citations), Atomic and Molecular Physics, and Optics (333 citations), Statistical and Nonlinear Physics (63 citations) and Computational Mechanics (48 citations). Yong Siah Teo has collaborated with scholars based in South Korea, Czechia and Germany. Frequent co-authors include Hyunseok Jeong, Z. Hradil, J. Řeháček, Berthold‐Georg Englert, Huangjun Zhu, L. L. Sánchez-Soto, Gerd Leuchs, Seung-Woo Lee, B. Stoklasa and Dae‐Ro Ahn. Their work appears in journals such as Physical review. A, Physical Review Letters, Physical Review A, New Journal of Physics and Physical Review Research.
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.