Daniel K. Park
- Artificial Intelligence top 2%
- Atomic and Molecular Physics, and Optics top 10%
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Materials Chemistry
- Co-authors
- Francesco PetruccioneTak HurAdenilton J. da SilvaJune‐Koo Kevin RheeCarsten BlankJoonsuk HuhRaymond LaflammeJonathan Baugh
- Topics
- Quantum Computing Algorithms and Architecture (29 papers)Quantum Information and Cryptography (28 papers)Quantum and electron transport phenomena (9 papers)
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsAtomic and Molecular Physics, and Optics
- Partner nations
- South KoreaBrazilSouth Africa
In The Last Decade
Daniel K. Park
35 papers receiving 797 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 685
- Atomic and Molecular Physics, and Optics 224
- Computational Theory and Mathematics 183
- Electrical and Electronic Engineering 105
- Materials Chemistry 32
Countries citing papers authored by Daniel K. Park
This map shows the geographic impact of Daniel K. Park'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 K. Park with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel K. Park more than expected).
Fields of papers citing papers by Daniel K. Park
This network shows the impact of papers produced by Daniel K. Park. 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 K. Park. The network helps show where Daniel K. Park may publish in the future.
Co-authorship network of co-authors of Daniel K. Park
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel K. Park. A scholar is included among the top collaborators of Daniel K. Park 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 Daniel K. Park. Daniel K. Park 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 | 2 | |
| 3 | 1 | |
| 4 | 14 | |
| 5 | 1 | |
| 6 | 33 | |
| 7 | 4 | |
| 8 | 11 | |
| 9 | 11 | |
| 10 | 24 | |
| 11 | 13 | |
| 12 | Quantum convolutional neural network for classical data classificationbreakdown → | 185 |
| 13 | 108 | |
| 14 | 39 | |
| 15 | 87 | |
| 16 | 29 | |
| 17 | 11 | |
| 18 | 24 | |
| 19 | College knowledge: An assessment of urban students’ awareness of college processes | 3 |
| 20 | 5 |
About Daniel K. Park
Daniel K. Park is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 38 papers that have together received 822 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (29 papers), Quantum Information and Cryptography (28 papers) and Quantum and electron transport phenomena (9 papers). The work is most often cited by research in Artificial Intelligence (685 citations), Computational Theory and Mathematics (183 citations) and Atomic and Molecular Physics, and Optics (224 citations). Daniel K. Park has collaborated with scholars based in South Korea, Brazil and South Africa. Frequent co-authors include Francesco Petruccione, Tak Hur, Adenilton J. da Silva, June‐Koo Kevin Rhee, Carsten Blank, Joonsuk Huh, Raymond Laflamme, Jonathan Baugh, Gina Passante and Guanru Feng. Their work appears in journals such as Physical Review Letters, Scientific Reports and Journal of Neurochemistry.
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.