Hsin-Yuan Huang
About
In The Last Decade
Hsin-Yuan Huang
44 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 1.8k
- Atomic and Molecular Physics, and Optics 1.1k
- Computational Mechanics 513
- Computational Theory and Mathematics 210
- Electrical and Electronic Engineering 205
Countries citing papers authored by Hsin-Yuan Huang
This map shows the geographic impact of Hsin-Yuan Huang'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 Hsin-Yuan Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hsin-Yuan Huang more than expected).
Fields of papers citing papers by Hsin-Yuan Huang
This network shows the impact of papers produced by Hsin-Yuan Huang. 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 Hsin-Yuan Huang. The network helps show where Hsin-Yuan Huang may publish in the future.
Co-authorship network of co-authors of Hsin-Yuan Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Hsin-Yuan Huang. A scholar is included among the top collaborators of Hsin-Yuan Huang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hsin-Yuan Huang. Hsin-Yuan Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 24 | |
| 5 | 13 | |
| 6 | 10 | |
| 7 | 12 | |
| 8 | 19 | |
| 9 | 6 | |
| 10 | 3 | |
| 11 | 37 | |
| 12 | Preparing random states and benchmarking with many-body quantum chaos breakdown → | 97 |
| 13 | Quantum advantage in learning from experiments breakdown → | 298 |
| 14 | Provably efficient machine learning for quantum many-body problems breakdown → | 150 |
| 15 | The randomized measurement toolbox breakdown → | 208 |
| 16 | Generalization in quantum machine learning from few training data breakdown → | 244 |
| 17 | Challenges and opportunities in quantum machine learning breakdown → | 296 |
| 18 | Emergent Randomness and Benchmarking from Many-Body Quantum Chaos | 6 |
| 19 | Predicting Features of Quantum Systems using Classical Shadows. | 1 |
| 20 | FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension | 26 |
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.