Nan Ding
- Artificial Intelligence top 2%
- Quantum Computing Algorithms and Architecture 2
- Advanced Computational Techniques and Applications 1
- Metaheuristic Optimization Algorithms Research 1
- Machine Learning and Algorithms 1
- Bayesian Modeling and Causal Inference 1
- Hardware and Architecture top 10%
-
- Advanced Bandit Algorithms Research 2
-
- Financial Markets and Investment Strategies 1
-
- Transportation Planning and Optimization 1
- Co-authors
- Hartmut NevenVadim SmelyanskiySergio BoixoMichael J. BremnerSergei V. IsakovJohn M. MartinisJiang ZhangRyan Babbush
- Journals
- Nature Physics (1 paper)Journal of Coastal Research (1 paper)Journal of Computer Science and Technology (1 paper)
- Partner nations
- ChinaUnited StatesSwitzerland
In The Last Decade
Nan Ding
9 papers receiving 662 citations
Hit Papers
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 594
- Computational Mathematics 7
- Atomic and Molecular Physics, and Optics 334
- Computational Theory and Mathematics 114
- Hardware and Architecture 38
Countries citing papers authored by Nan Ding
This map shows the geographic impact of Nan Ding'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 Nan Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nan Ding more than expected).
Fields of papers citing papers by Nan Ding
This network shows the impact of papers produced by Nan Ding. 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 Nan Ding. The network helps show where Nan Ding may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Nan Ding, 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 | 2023 | 5 | |
| 2 | 2019 | 1 | |
| 3 | Characterizing quantum supremacy in near-term devicesbreakdown → | 2018 | 628 |
| 4 | Cold-Start Reinforcement Learning with Softmax Policy Gradients | 2017 | 7 |
| 5 | 2015 | 9 | |
| 6 | 2014 | 1 | |
| 7 | 2012 | 17 | |
| 8 | 2008 | 21 | |
| 9 | 2008 | 2 |
About Nan Ding
Nan Ding is a scholar working on Management Science and Operations Research, Artificial Intelligence, Transportation, Industrial and Manufacturing Engineering and Finance, having authored 9 papers that have together received 691 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (2 papers), Quantum Computing Algorithms and Architecture (2 papers), Advanced Computational Techniques and Applications (1 paper), Financial Markets and Investment Strategies (1 paper), Metaheuristic Optimization Algorithms Research (1 paper), Transportation Planning and Optimization (1 paper), Machine Learning and Algorithms (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (594 citations), Computational Mathematics (7 citations), Atomic and Molecular Physics, and Optics (334 citations), Computational Theory and Mathematics (114 citations) and Hardware and Architecture (38 citations). Nan Ding has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Hartmut Neven, Vadim Smelyanskiy, Sergio Boixo, Michael J. Bremner, Sergei V. Isakov, John M. Martinis, Jiang Zhang, Ryan Babbush, Zengqi Sun and Radu Soricut. Their work appears in journals such as Nature Physics, Journal of Coastal Research, Journal of Computer Science and Technology, 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) and arXiv (Cornell University).
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.