Daniel K. Park

1.4k total citations · 1 hit paper
38 papers, 822 citations indexed

About

Daniel K. Park is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Daniel K. Park has authored 38 papers receiving a total of 822 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 16 papers in Atomic and Molecular Physics, and Optics and 8 papers in Computational Theory and Mathematics. Recurrent topics in Daniel K. Park's work include Quantum Computing Algorithms and Architecture (29 papers), Quantum Information and Cryptography (28 papers) and Quantum and electron transport phenomena (9 papers). Daniel K. Park is often cited by papers focused on Quantum Computing Algorithms and Architecture (29 papers), Quantum Information and Cryptography (28 papers) and Quantum and electron transport phenomena (9 papers). Daniel K. Park collaborates with scholars based in South Korea, Brazil and South Africa. Daniel K. Park's 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 and has published in prestigious journals such as Physical Review Letters, Scientific Reports and Journal of Neurochemistry.

In The Last Decade

Daniel K. Park

35 papers receiving 797 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Quantum convolutional neural network for classical data classification

2022 185 citations Tak Hur, Daniel K. Park et al. Quantum Machine Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel K. Park South Korea	15	685	224	183	105	32	38	822
Iris Cong United States	9	931 1.4×	412 1.8×	193 1.1×	141 1.3×	56 1.8×	10	1.1k
Johannes Jakob Meyer Germany	9	885 1.3×	332 1.5×	148 0.8×	88 0.8×	48 1.5×	16	972
Ryan Sweke Germany	11	795 1.2×	326 1.5×	122 0.7×	80 0.8×	37 1.2×	19	879
Vojtěch Havlíček Czechia	7	1.2k 1.7×	403 1.8×	240 1.3×	143 1.4×	79 2.5×	17	1.4k
Kunal Sharma United States	13	1.1k 1.5×	460 2.1×	168 0.9×	109 1.0×	54 1.7×	33	1.2k
Alba Cervera-Lierta Canada	8	1.1k 1.5×	567 2.5×	185 1.0×	109 1.0×	80 2.5×	11	1.3k
Marcello Benedetti United Kingdom	11	875 1.3×	291 1.3×	179 1.0×	107 1.0×	40 1.3×	20	955
Alex Bocharov United States	10	815 1.2×	241 1.1×	209 1.1×	129 1.2×	34 1.1×	15	964
Josh Izaac Australia	14	1.4k 2.0×	534 2.4×	218 1.2×	200 1.9×	61 1.9×	20	1.5k
Leonard Wossnig United Kingdom	9	1.0k 1.5×	459 2.0×	245 1.3×	93 0.9×	57 1.8×	15	1.2k

Countries citing papers authored by Daniel K. Park

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Park, Daniel K., et al.. (2025). Expressivity of deterministic quantum computation with one qubit. Physical review. A. 111(2).

Lee, Chang Won, et al.. (2025). Optimizing quantum convolutional neural network architectures for arbitrary data dimension. Frontiers in Physics. 13. 2 indexed citations

Hur, Tak, Daniel K. Park, Na‐Young Shin, et al.. (2024). Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series. Knowledge-Based Systems. 310. 112922–112922. 1 indexed citations

Sinayskiy, Ilya, et al.. (2024). The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices. Scientific Reports. 14(1). 16011–16011. 14 indexed citations

Lim, Hyang‐Tag, et al.. (2024). Distributed quantum machine learning via classical communication. Quantum Science and Technology. 10(1). 15059–15059. 1 indexed citations

Huh, Joonsuk, et al.. (2023). Classical-to-quantum convolutional neural network transfer learning. Neurocomputing. 555. 126643–126643. 33 indexed citations

Lee, Dongkeun, et al.. (2023). Variational quantum state discriminator for supervised machine learning. Quantum Science and Technology. 9(1). 15017–15017. 4 indexed citations

Park, Gunhee, Joonsuk Huh, & Daniel K. Park. (2023). Variational quantum one-class classifier. Machine Learning Science and Technology. 4(1). 15006–15006. 11 indexed citations

Sinayskiy, Ilya, et al.. (2023). Hierarchical quantum circuit representations for neural architecture search. npj Quantum Information. 9(1). 11 indexed citations

10.

Park, Daniel K., et al.. (2023). Configurable sublinear circuits for quantum state preparation. Quantum Information Processing. 22(2). 24 indexed citations

11.

Blank, Carsten, et al.. (2022). Compact quantum kernel-based binary classifier. Quantum Science and Technology. 7(4). 45007–45007. 13 indexed citations

12.

Hur, Tak, et al.. (2022). Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence. 4(1). 185 indexed citations breakdown →

13.

Park, Daniel K., et al.. (2021). A divide-and-conquer algorithm for quantum state preparation. Scientific Reports. 11(1). 6329–6329. 108 indexed citations

14.

Park, Daniel K., et al.. (2020). Quantum Error Mitigation With Artificial Neural Network. IEEE Access. 8. 188853–188860. 39 indexed citations

15.

Park, Daniel K., Francesco Petruccione, & June‐Koo Kevin Rhee. (2019). Circuit-Based Quantum Random Access Memory for Classical Data. Scientific Reports. 9(1). 3949–3949. 87 indexed citations

16.

Feng, Guanru, Joel J. Wallman, Daniel K. Park, et al.. (2016). Estimating the Coherence of Noise in Quantum Control of a Solid-State Qubit. Physical Review Letters. 117(26). 260501–260501. 29 indexed citations

17.

Park, Daniel K., Guanru Feng, Robabeh Rahimi, Jonathan Baugh, & Raymond Laflamme. (2016). Randomized benchmarking of quantum gates implemented by electron spin resonance. Journal of Magnetic Resonance. 267. 68–78. 11 indexed citations

18.

Park, Daniel K., et al.. (2010). Tyrosine phosphatases Shp1 and Shp2 have unique and opposing roles in oligodendrocyte development. Journal of Neurochemistry. 113(1). 200–212. 24 indexed citations

19.

Hallett, Ronald E., et al.. (2008). College knowledge: An assessment of urban students’ awareness of college processes. Scholarly Commons (University of the Pacific). 84(2). 10–17. 3 indexed citations

20.

Jung, Eylee, Mi-Ra Hwang, Daniel K. Park, et al.. (2008). Amplitude damping for single-qubit system with single-qubit mixed-state environment. Journal of Physics A Mathematical and Theoretical. 41(4). 45306–45306. 5 indexed citations

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.

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