Daniel K. Park
- Artificial Intelligence top 2%
- Quantum Computing Algorithms and Architecture 29
- Quantum Information and Cryptography 28
- Neural Networks and Reservoir Computing 3
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- Quantum-Dot Cellular Automata 7
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- Quantum and electron transport phenomena 9
- Quantum Mechanics and Applications 4
- Atomic and Subatomic Physics Research 3
- Biophysics top 10%
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- Advancements in Semiconductor Devices and Circuit Design 5
- Co-authors
- Francesco PetruccioneTak HurAdenilton J. da SilvaJune‐Koo Kevin RheeCarsten BlankJoonsuk HuhRaymond LaflammeJonathan Baugh
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsAtomic and Molecular Physics, and Optics
- Journals
- Physical review. A (5 papers)Quantum Science and Technology (3 papers)Scientific Reports (3 papers)
- 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
- Computational Theory and Mathematics 183
- Atomic and Molecular Physics, and Optics 224
- Computational Mathematics 3
- Biophysics 26
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
The 25 scholars most cited alongside Daniel K. Park, 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 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 14 | |
| 5 | 2024 | 1 | |
| 6 | 2023 | 33 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 11 | |
| 10 | 2023 | 24 | |
| 11 | 2022 | 13 | |
| 12 | Quantum convolutional neural network for classical data classificationbreakdown → | 2022 | 185 |
| 13 | 2021 | 108 | |
| 14 | 2020 | 39 | |
| 15 | 2019 | 87 | |
| 16 | 2016 | 29 | |
| 17 | 2016 | 11 | |
| 18 | 2010 | 24 | |
| 19 | College knowledge: An assessment of urban students’ awareness of college processes | 2008 | 3 |
| 20 | 2008 | 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), Quantum and electron transport phenomena (9 papers), Quantum-Dot Cellular Automata (7 papers), Advancements in Semiconductor Devices and Circuit Design (5 papers), Quantum Mechanics and Applications (4 papers), Atomic and Subatomic Physics Research (3 papers) and Neural Networks and Reservoir Computing (3 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. A, Quantum Science and Technology, Scientific Reports, Machine Learning Science and Technology and Physical Review Letters.
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.