Quantum information theory and algorithms.

I am an undergraduate researcher in the School of Computing at KAIST. My research is mostly in theoretical quantum information, especially problems where learning theory, algorithms, and property testing meet. Recently I have been thinking about how much information is needed to learn or certify quantum systems, and how the structure of a Hamiltonian or a quantum process changes what is computationally possible.

My research background includes work with the Research Institute of Mathematics at Seoul National University and the Quantum Information Theory Group in the Department of Physics at KAIST. These collaborations shaped much of my current taste: I like problems that start from a concrete quantum information task, but require careful algorithmic or complexity-theoretic reasoning to understand properly.

In 2026, I was accepted to the Caltech SURF program, where I am working with Yu Tong and John Preskill on Hamiltonian learning with static single-qubit fields, gates, and measurements. This project connects my interests in certifying and learning Hamiltonians in the Heisenberg limit with algorithms that remain meaningful under realistic control constraints.

My recent work, “Heisenberg-limited Hamiltonian learning without short-time control,” was accepted as a long talk at AQIS 2026.

Beyond my current focus on Hamiltonian learning, quantum algorithms, and quantum property testing, I have also worked on quantum entropy estimation, variational quantum algorithms, quantum machine learning and its applications, quantum error correction, and gradient clipping methods for large language models.

I enjoy conversations that turn half-formed research questions into concrete problems. If you want to discuss quantum learning, algorithms, sensing, or early-stage startup ideas, feel free to reach out.