About my Website and Me

Introduction

This website is to list what I have done. Here is my CV, for more detailed informations.

Research interest

• Quantum information science and quantum computing
• Machine learning and optimization

Education

• Cornell University
  • PhD candidate in physics
• Gwangju Institute of Science & Technology (GIST college website)
  • B.S. Physics, minor in Math

Research Experience

2026

• 26-spring

2025

• 25-fall

2024

• 24-fall to 25-spring: Learning the phases of monitored quantum dynamics
  • Developed a scalable, data-centric framework for detecting measurement-induced phase transitions (MIPTs) in monitored circuits without post-selection or classical simulation, enabling experimental observability on near-term quantum hardware
  • Experimental collaboration with Quantinuum in the ongoing follow-up work.
  • Manuscript under review in PRX Intelligence: H. Kim, A. Kumar, Y. Zhou, Y. Xu, R. Vasseur, E.-A. Kim, Learning measurement-induced phase transitions using attention, arXiv:2508.15895
  

2022

• 22-fall to 24-spring: Attention to Quantum Complexity
  • Introduce Quantum Attention Network (QuAN) that leverages the power of attention mechanisms driving large language models (LLM) to characterize complexity of quantum states from bit string data.
  • Resolved three open problems (i) witnessing entanglement scaling transition, (ii) detecting the growth of quantum complexity in deep random quantum circuit despite the noise, (iii) revealing mixed-state topological order of toric code.
  • Collaboration with Google Quantum AI and MIT.

  • Published in Science Advances: H. Kim, Y. Zhou, Y. Xu, K. Varma, A. H. Karamlou, I. T. Rosen, J. C. Hoke, C. Wan, J. P. Zhou, W. D. Oliver, Y. D. Lensky, K. Q. Weinberger, E.-A. Kim, Attention to Quantum Complexity, Sci. Adv. 11, eadu0059 (2025).

    

2021

• 21-fall to 22-spring
  • Interpretation on minimal neural network to learn the metal-insulator transition in the dynamical mean-field theory. Analyzing the network connectivity to find the system measure that can be employed to broader range beyond our specific setup, based on the few bath orbitals from DMFT-ED.
  • Published: H. Kim, D. Kim, D.-H. Kim, Minimal Neural Network to Learn the Metal-insulator Transition in the Dynamical Mean-field Theory, NPSM 2022, 72, 487-494 (2022).
  • (github, summary)
  
• 21-summer
  • Machine learning prediction of metal-insulator transition in DMFT Hubbard model (github, summary)
• 21-spring

  • Undergraduate thesis 1: Calculating the hybridization function on DMFT-NRG, trying to improve the accuracy of the metal-insulator transition points.

    

2020

• 20-summer

  • Summer Undergraduate Research Fellowship (G-SURF): Markov-chain-MonteCarlo method and Ising model + Self-learning Monte-Carlo (github, summary_slmc, summary_ising)

    

2019

• 19-winter
  • AdS-CFT with deep learning: Tensorflow (github)

Extra curriculum

2025

• 25-fall
  • Teaching Assisntant for PHYS 2214 Oscillations, Waves, and Quantum Physics. Manage discussion sections, lab sections, homework, and exams.

    2022

• 22-fall
  • Teaching Assistant for PHYS 2213 Electromagnetism: Manage discussion sections, homework, and exams.

2021

• 21-fall
  • Teaching Assistant for General Physics I in English
  • Presentation in Nuclear & Particle Physics: (1) Renormalization group, (2) Neutrino Oscillation
• 21-spring
  • Teaching Assistant for General Physics I
  • Project on Machine learning & Deep learning: implementing neural network classifier for CIFAR100 dataset on Kaggle

2020

• 20-fall
  • Presentation in Thermal & Statistical Physics: Transverse-field Ising model (summary)
• 20-spring
  • Teaching Assistant for General Physics I
  • Project in Quantum Physics I: Exact solution of finite harmonic oscillator (summary)

2019

• 19-summer
  • Boston University summer international exchange: Introductory Microeconomic Analysis, Basic Statistics and Probability
• 19-spring
  • Presentation in Physics & Math Seminar: Coanda effect (summary)

2018

• 18-winter
  • Caltech collaboration course, Physical Biology of the Cell (PBoC)

2016-7

• Korean Youth Physicists’ Tournament (KYPT): Fast chain, Spiral wave, Torsion gyroscope