• Neural quantum embedding via deterministic quantum computation with one qubit
  Hongfeng Liu, Tak Hur, Shitao Zhang, Liangyu Che, Xinyue Long, Xiangyu Wang, Keyi Huang, Yu-ang Fan, Yuxuan Zheng, Yufang Feng, Xinfang Nie, Daniel K. Park*, Dawei Lu
  arXiv:2501.15359 [quant-ph]

• Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling
  Nayeli A. Rodriguez-Briones, and Daniel K. Park*.
  arXiv:2501.02687 [quant-ph]; [cs.LG] 

• Schmidt quantum compressor
  Israel F. Araujo, Hyeondo Oh, Nayeli A. Rodriguez-Briones, and Daniel K. Park*.
  arXiv:2412.16337 [quant-ph]; [cs.ET]

• Understanding generalization in quantum machine learning with margins
  Tak Hur, and Daniel K. Park*.
  arXiv:2411.06919 [quant-ph]; [cs.LG]

• Quantum-classical hybrid approach for codon optimization and its practical applications.
  You Kyoung Chung, Dongkeun Lee, Junho Lee, Jaehee Kim, Daniel K. Park, and Joonsuk Huh
  bioRxiv 2024.06.08.598046

• Quantum-inspired classification via efficient simulation of Helstrom measurement.
  Wooseop Hwang, Daniel K. Park, Israel F. Araujo, and Carsten Blank.
  arXiv:2403.15308 [quant-ph]

• Quantum state discrimination for supervised classification.
  Roberto Giuntini, Hector Freytes, Daniel K. Park*, Carsten Blank, Federico Holik, Keng Loon Chow, and Giuseppe Sergioli.
  arXiv:2104.00971 [quant-ph].

[43] Hamiltonian formulations of centroid-based clustering.
Myeonghwan Seong, and Daniel K. Park*.
Frontiers in Physics, 13 1544623 (2025)

[42] Optimizing quantum convolutional neural network architectures for arbitrary data dimension.
Changwon Lee, Israel F. Araujo, Dongha Kim, Junghan Lee, Siheon Park, Ju-Young Rye, and Daniel K. Park*.
Frontiers in Physics, 13 1529188 (2025)

[41] Expressivity of deterministic quantum computation with one qubit.
Yujin Kim, and Daniel K. Park*.
Phys. Rev. A 111, 022429 (2025)

[40] Early-stage detection of cognitive imparement by hybrid quantum-classical algorithm using resting-state functional MRI time-series.
Junggu Choi, Tak Hur, Daniel K. Park, Na-Young Shin, Seung-Koo Lee, Hakbae Lee, and Sanghoon Han.
Knowledge-Based Systems 310 112922 (2025)

[39] Distributed quantum machine learning via classical communication.
Kiwmann Hwang, Hyang-Tag Lim, Yong-Su Kim, Daniel K. Park, and Yosep Kim
Quantum Science and Technology 10 015059 (2024)

[38] Quantum support vector data description for anomaly detection.
Hyeondo Oh and Daniel K. Park*.
Machine Learning: Science and Technology 5 035052 (2024)

[37] Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning.
Tak Hur, Israel F. Araujo, and Daniel K. Park*.
Phys. Rev. A 110, 022411 (2024)

[36] The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices.
Aidan Pellow-Jarman, Shane McFarthing, Ilya Sinayskiy, Daniel K. Park, Anban Pillay, and Francesco Petruccione.
Scientific Reports 14 16011 (2024)

[35 ] Quadratic speed-ups in quantum kernelized binary classification.
Jungyun Lee and Daniel K. Park*.
Advanced Quantum Technologies 2400126 (2024)

[34] Quantum variational distance-based centroid classifier.
Nicolas M. de Oliveira, Daniel K. Park, Israel F. Araujo, and Adenilton J. da Silva.
Neurocomputing 576 127356 (2024).

[33] Scalable quantum measurement error mitigation via conditional independence and transfer learning.
Changwon Lee and Daniel K. Park*.
Machine Learning: Science and Technology 4 045051 (2023).

[32] Variational quantum state discriminator for supervised machine learning.
Dongkeun Lee, Kyunghyun Baek, Joonsuk Huh, and Daniel K. Park*.
Quantum Science and Technology 9 015017 (2023).

[31] Classical-to-quantum convolutional neural network transfer learning.
Juhyeon Kim, Joonsuk Huh, and Daniel K. Park*.
Neurocomputing 555 126643 (2023).

[30] Hierarchical quantum circuit representations for neural architecture search.
Matt Lourens, Ilya Sinayskiy, Daniel K. Park, Carsten Blank, and Francesco Petruccione.
npj Quantum Information 9, 79 (2023).

[29] Variational quantum approximate support vector machine with inference transfer.
Siheon Park, Daniel K. Park, and June-Koo Rhee.
Scientific Reports 13 3288 (2023).

[28] Configurable sublinear circuits for quantum state preparation.
Israel. F. Araujo, Daniel K. Park, Teresa B Ludermir, Wilson R Oliveira, Francesco Petruccione, and Adenilton J. da Silva.
Quantum Information Processing 22 123 (2023) .

[27] Variational quantum one-class classifier.
Gunhee Park, Joonsuk Huh, and Daniel K. Park*.
Machine Learning: Science and Technology 4 015006 (2023).

[26] Quantum-inspired algorithm for direct multi-class classification.
Roberto Giuntini, Federico Holik, Daniel K. Park*, Hector Freytes, Carsten Blank, and Giuseppe Sergioli.
Applied Soft Computing 134 109956 (2023).

[25] Linear-depth quantum circuits for multi-qubit controlled gates.
Adenilton J. da Silva and Daniel K. Park*.
Phys. Rev. A 106 042602 (2022).

[24] Quantum readout error mitigation via deep learning.
Jihye Kim, Byungdu Oh, Yonuk Chong, Euyhyeon Hwang, Daniel K. Park*.
New J. Phys. 24 073009 (2022).

[23] Compact quantum kernel-based binary classifier.
Carsten Blank, Adenilton J. da Silva, Lucas P. de Albuquerque, Francesco Petruccione, and Daniel K. Park*.
Quantum Science and Technology 7 045007 (2022).

[22] Quantum convolutional neural network for classical data classification.
Tak Hur, Leeseok Kim, and Daniel K. Park*.
Quantum Machine Intelligence 4, 3 (2022). 

[21] Quantum-enhanced analysis of discrete stochastic processes.
Carsten Blank, Daniel K. Park, and Francesco Petruccione.
npj Quantum Information 7, 126 (2021).

[20] A divide-and-conquer algorithm for quantum state preparation.
Israel. F. Araujo, Daniel K. Park, Francesco Petruccione, and Adenilton J. da Silva.
Scientific Reports 11 6329 (2021). **Editor’s Choice & Top 20 in Physics, 2021 (within Sci. Rep.).

[19] Circuit-based quantum random access memory for classical data with continuous amplitudes.
Tiago Veras, Ismael Araujo, Daniel K. Park, Adenilton. J. da Silva.
IEEE Transactions on Computers 70 12, 2125-2135 (2021).

[18] Quantum error mitigation with artificial neural network.
Changjun Kim, Daniel K. Park and June-Koo Rhee.
IEEE Access 8 188853 (2020).

[17] Quantum-classical hybrid reinforcement learning for decoding noisy classical parity information.
Daniel K. Park*, Jonghun Park and June-Koo Rhee.
Quantum Machine Intelligence 2, 8 (2020).

[16] Nanometer-Scale Water Dynamics in Nafion Polymer Electrolyte Membranes: Influence of Molecular Hydrophobicity and Water Content Revisited.
Seung-Bo Saun, Jiwon Kim, Ryeo Yun Hwang, Yeonho Ahn, Dukjoon Kim, Daniel K. Park, Soonchil Lee and Oc Hee Han.
ACS Macro Letters 9, 7, 1013-1018 (2020).

[15] The theory of the quantum kernel-based binary classifier.
Daniel K. Park*, Carsten Blank and Francesco Petruccione.
Physics Letters A 384, 21, 126422 (2020).

[14] Quantum classifier with tailored quantum kernel.
Carsten Blank, Daniel K. Park, June-Koo Rhee and Francesco Petruccione.
npj Quantum Information 6, 41 (2020).

[13] Parallel quantum trajectories via forking for sampling without redundancy.
Daniel K. Park, Ilya Sinayskiy, Mark Fingerhuth, Francesco Petruccione, and June-Koo Rhee.
New J. Phys. 21 083024 (2019).

[12] Circuit-based quantum random access memory for classical data.
Daniel K. Park, Francesco Petruccione, and June-Koo Rhee.
Scientific Reports 9 3949 (2019).

[11] Electron spin relaxations of phosphorus donors in bulk silicon under large electric field.
Daniel K. Park, Sejun Park, Hyejung Jee, and Soonchil Lee.
Scientific Reports 9 2951 (2019).

[10] Efficient continuous wave noise spectroscopy beyond weak coupling.
Kyle Willick, Daniel K. Park, and Jonathan Baugh.
Phys. Rev. A 98 013414 (2018).

[9] Noise-tolerant parity learning with one quantum bit.
Daniel K. Park*, June-Koo Rhee, and Soonchil Lee.
Phys. Rev. A 97 032327 (2018).

[8] Estimating the coherence of noise in quantum control of a solid-state qubit.
Guanru Feng, Joel Wallman, Brandon Buonacorsi, Franklin H. Cho, Daniel K. Park, Tao Xin, Dawei Lu, Jonathan Baugh, and Raymond Laflamme.
Phys. Rev. Lett. 117 260501 (2016).

[7] Randomized benchmarking of quantum gates implemented by electron spin resonance.
Daniel K. Park, Guanru Feng, Robabeh Darabad, Jonathan Baugh, and Raymond Laflamme.
Journal of Magnetic Resonance 267 68 (2016).

[6] Hyperfine spin qubits in irradiated malonic acid: heat-bath algorithmic cooling.
Daniel K. Park, Guanru Feng, Robabeh Darabad, Stephane Labruyere, Taiki Shibata, Shigeaki Nakazawa, Kazunobu Sato, Takeji Takui, Raymond Laflamme and Jonathan Baugh.
Quantum Inf. Process. 14 2435 (2015).

[5] Three paths interference using nuclear magnetic resonance: A test of the consistency of Born’s rule.
Daniel K. Park, Osama Moussa and Raymond Laflamme.
New J. Phys. 14 113025 (2012).

[4] Recent advances in nuclear magnetic resonance quantum information processing.
Ben Criger, Gina Passante, Daniel K. Park and Raymond Laflamme.
Phil. Trans. R. Soc. A 370, 4620 (2012).

[3] Robust quantum classifier with minimal overhead.
Daniel K. Park*, Carsten Blank and Francesco Petruccione.
IEEE Proceedings of International Joint Conference on Neural Networks 2021 pp. 1-7, doi: 10.1109/IJCNN52387.2021.9533403.

[2] Heat bath algorithmic cooling with spins: review and prospects.
Daniel K. Park, Nayeli Rodriguez-Briones, Guanru Feng, Robabeh Darabad, Jonathan Baugh, and Raymond Laflamme.
Electron Spin Resonance (ESR) Based Quantum Computing, Biological Magnetic Resonance Vol. 31 pp 227–255, eds. T. Takui, G. Hansen and L. Berliner (2015).

[1] Few-qubit magnetic resonance quantum information processors: Simulating chemistry and physics.
Ben Criger, Daniel K. Park and Jonathan Baugh.
Quantum Information and Computation for Chemistry: Advances in Chemical Physics, eds. S. Rice and S. Kais (2013).