Mutual Information Maximizing Quantum GANs
Designing quantum generative adversarial networks that maximize mutual information to stabilize training and improve sample fidelity (Scientific Reports 15, 32835, 2025).
4th-year undergrad at SNU, exchange at UC Davis. Research in quantum complexity and quantum machine learning.
Interests Quantum Computing · Problem Solving · Machine Learning · Quant Investment
Four threads I keep coming back to.
Designing quantum generative adversarial networks that maximize mutual information to stabilize training and improve sample fidelity (Scientific Reports 15, 32835, 2025).
Building disentangled quantum neural networks for unified estimation of quantum entropies and distance measures (Physical Review A 110, 062418, 2024).
Developing transformer-based time-series models for low-cost ETF portfolio management and index tracking, extending AI-based ETF portfolio research from NCSOFT internship and competition work toward liquidity-aware strategies.
Code, dashboards, and small harnesses.
A minimal harness for hardware-aware VQE with a fixed wall-clock budget and one editable training file.
Live simple-ROI record for a cross-exchange arbitrage strategy, regenerated from CSV and rendered as paper-toned figures.
A single-qubit state lab panel with θ/φ controls, gate actions, and a trajectory trace.
Always happy to talk about anything.