Daniel Bochen Tan
Impact in
- Artificial Intelligence top 10%
- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
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- Parallel Computing and Optimization Techniques
Papers in
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- Quantum Computing Algorithms and Architecture 11
- Quantum Information and Cryptography 10
- Neural Networks and Reservoir Computing 1
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- Quantum Mechanics and Applications 2
- Cold Atom Physics and Bose-Einstein Condensates 1
- Co-authors
- Jason Cong (10 shared papers)Mikhail D. Lukin (2 shared papers)Dolev Bluvstein (2 shared papers)Wan-Hsuan Lin (4 shared papers)Nikolaj Bjørner (1 shared paper)Song Han (2 shared papers)Murphy Yuezhen Niu (1 shared paper)Jiaqi Gu (2 shared papers)
- Journals
- IEEE Journal on Emerging and Selected Topics in Circuits and Systems (1 paper)IEEE Transactions on Computers (1 paper)Quantum (1 paper)
- Partner nations
- United States
In The Last Decade
Daniel Bochen Tan
10 papers receiving 156 citations
Peers
Comparison fields: 5 of 13
- Artificial Intelligence 153
- Hardware and Architecture 27
- Computational Theory and Mathematics 44
- Atomic and Molecular Physics, and Optics 52
- Software 4
Countries citing papers authored by Daniel Bochen Tan
This map shows the geographic impact of Daniel Bochen Tan'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 Daniel Bochen Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Bochen Tan more than expected).
Fields of papers citing papers by Daniel Bochen Tan
This network shows the impact of papers produced by Daniel Bochen Tan. 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 Daniel Bochen Tan. The network helps show where Daniel Bochen Tan may publish in the future.
Co-authors
The 13 scholars most cited alongside Daniel Bochen Tan, 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 | 2020 | 56 | |
| 2 | 2021 | 22 | |
| 3 | 2024 | 18 | |
| 4 | 2023 | 18 | |
| 5 | 2022 | 15 | |
| 6 | 2022 | 12 | |
| 7 | 2024 | 10 | |
| 8 | 2024 | 7 | |
| 9 | 2025 | 3 | |
| 10 | 2024 | 1 | |
| 11 | 2025 | 0 |
About Daniel Bochen Tan
Daniel Bochen Tan is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Electrical and Electronic Engineering and Hardware and Architecture, having authored 11 papers that have together received 162 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (11 papers), Quantum Information and Cryptography (10 papers), Quantum-Dot Cellular Automata (3 papers), Parallel Computing and Optimization Techniques (2 papers), Low-power high-performance VLSI design (2 papers), Quantum Mechanics and Applications (2 papers), Cold Atom Physics and Bose-Einstein Condensates (1 paper) and Neural Networks and Reservoir Computing (1 paper). The work is most often cited by research in Artificial Intelligence (153 citations), Hardware and Architecture (27 citations), Computational Theory and Mathematics (44 citations), Atomic and Molecular Physics, and Optics (52 citations) and Software (4 citations). Daniel Bochen Tan has collaborated with scholars based in United States. Frequent co-authors include Jason Cong, Mikhail D. Lukin, Dolev Bluvstein, Wan-Hsuan Lin, Nikolaj Bjørner, Song Han, Murphy Yuezhen Niu, Jiaqi Gu, David Z. Pan and Umut A. Acar. Their work appears in journals such as IEEE Journal on Emerging and Selected Topics in Circuits and Systems, IEEE Transactions on Computers and Quantum.
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.