Minwoo James Kim

M.S. student in Computer Science & Engineering, Seoul National University

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I am Minwoo James Kim, an M.S. student in Computer Science & Engineering at Seoul National University, where I am a member of the Quantum Information and Quantum Computing Lab advised by Prof. Taehyun Kim. My research spans quantum machine learning, quantum information theory, the expressivity of quantum algorithms, quantum reservoir computing, and quantum computer architecture.

I received my B.S. in Physics from Seoul National University (GPA 3.53/4.00), where my bachelor’s thesis examined practical limitations in training a Variational Quantum-Neural Hybrid Eigensolver.

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Education

Seoul National University, Department of Computer Science & Engineering

M.S. in Computer Science & Engineering, 2025-Present
Quantum Information and Quantum Computing Lab
Advisor: Prof. Taehyun Kim

  • Research focus: quantum information, quantum machine learning, variational quantum algorithms, and quantum computing architectures.
  • Expected completion: February 2027.

Seoul National University, Department of Physics & Astronomy

B.S. in Physics, 2019-2025

  • Bachelor’s thesis: Practical Limitations on Training of Variational Quantum-Neural Hybrid Eigensolver.
  • GPA: 3.53/4.00.

Publications

  1. M. J. Kim, K. K. Park, K. Lee, J. Bang, and T. Kim. A Rigorous Hybridization of Variational Quantum Eigensolver with Classical Neural Network. arXiv:2602.17295, 2026.
  2. M. J. Kim, J. Bang, and T. Kim. Measurement-Limited Expressivity in Quantum Reservoir Computing. Manuscript in preparation.
  3. M. J. Kim, K. K. Park, B. Cho, and T. Kim. Architecture of a Modular Operating System for High-Level Quantum Computation. Poster, IEEE QCE 2025, Art. no. 11249975.
  4. K. K. Park, M. J. Kim, B. Cho, and T. Kim. Quantum Circuit Compilation for Small Scale Trapped Ion Quantum Computer. Poster, IEEE QCE 2025, Art. no. 11250081.
  5. K. K. Park, K. Choi, M. J. Kim, G. Song, and T. Kim. Quantum Linear Multistep Method for Using a Quantum Oracle with Differential Equations. arXiv:2501.03781, 2025.

Presentations

  • A Rigorous Hybridization of Variational Quantum Eigensolver and Classical Neural Network, poster presentation at AQIS 2026.
  • Quantum Reservoir Computing Using Prethermal Floquet Dynamics, expected oral presentation at the KPS 2026 Fall Meeting.
  • Architecture of a Modular Operating System for High-level Quantum Computation, poster presentation at IEEE Quantum Week 2025.
  • Practical limitations on training of Variational Quantum-Neural Hybrid Eigensolver, oral presentation at QISK 2025 Annual Meeting.

Research and Software

Quantum-Neural Hybrid Variational Algorithms

2024-2026

  • Developed and analyzed diagonal neural post-processing methods for variational quantum eigensolvers, with attention to expressivity, stability, and finite-shot limitations.
  • Studied non-unitary structural limitations and developed a unitary variant designed for shot-based estimation.
  • Implemented numerical experiments with Qiskit and PyTorch. Software: VQNHELib.

Quantum Reservoir Computing

2026-Present

  • Analyzed how fixed measurement and readout constraints limit accessible latent spaces in quantum reservoir computing models.
  • Developed theoretical criteria relating reservoir dynamics, visible operator subspaces, and expressivity.
  • Implemented simulation and expressivity-diagnostic tools. Software: PyQRes.

Trapped-Ion Quantum-Computer Architecture and Software

2025-Present

  • Designed a modular user-level execution architecture spanning compilation, scheduling, hardware dispatch, and result processing.
  • Developed a Qiskit-compatible Python client for submitting circuits to laboratory hardware and retrieving results: PyQCSNU.
  • Contributed to experimental control software used in the QuIQCL laboratory.

Selected Software

  • VQNHELib (2024-2026): Qiskit/PyTorch implementation of diagonal non-unitary post-processing algorithms introduced in arXiv:2602.17295. Repository: github.com/miinukim/vqnhelib
  • PyQRes (2026-Present): Simulation tools for quantum reservoir models and expressivity diagnostics. Repository: github.com/miinukim/pyqres
  • PyQCSNU (2025-Present): Qiskit-compatible client for submitting quantum circuits to trapped-ion laboratory hardware and retrieving experimental results. Repository: github.com/snu-quiqcl/pyqcsnu

Patents, Mentorship, and Honors

  • Patent: T. Kim, M. Kim. Methods and Apparatus for Resource-Efficient Calculation of Ground State of Quantum Systems, and Recording Medium Thereof. Korean Patent Application No. SNU202505234, filed by Seoul National University R&DB Foundation, 2025.
  • Presidential Science Scholarship, Korea Student Aid Foundation, 2019-2024.
  • Qiskit Advocate, 2025-Present.
  • IBM Quantum Special Award, QHackathon 2024, Jun 2024.
  • Quantum Excellence, Qiskit Global Summer School, Aug 2023.
  • Center Director Award, QHackathon 2023, Jun 2023.

Technical Skills

  • Programming: Python, C/C++, Java, OCaml
  • Quantum computing: Qiskit, PennyLane
  • Scientific computing: NumPy, PyTorch
  • Systems and tools: Linux, Git, LaTeX, Conda, Django, FastAPI
  • Languages: Korean (native), English (TOEFL iBT 110/120)