Tak Hur.

Quantum computing · Machine learning

Tak Hur

I am a postdoctoral researcher at the University of Waterloo. My research explores how machine learning can help us understand, use, and protect quantum systems.

I completed my PhD in Statistics and Data Science at Yonsei University in 2026. My work spans quantum machine learning, quantum error correction, and neural quantum states, with an interest in both theory and practical algorithms.

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News

  1. My preprint on stochastic reconfiguration as statistical filtering is on arXiv.
  2. I started as a postdoctoral researcher at the University of Waterloo.
  3. I gave a contributed talk on scalable neural decoders at ML4QT.
  4. I completed my PhD in Statistics and Data Science at Yonsei University.
  5. Our project on mixture-of-experts decoders received an NVIDIA Academic Grant.

Selected papers

All publications
  1. 2026

    Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States

    Tak Hur · arXiv:2609.23334

  2. 2025

    Neural Quantum Embedding via Deterministic Quantum Computation with One Qubit

    Hongfeng Liu, Tak Hur, Shitao Zhang, et al. · Physical Review Letters 135, 080603

  3. 2025

    Understanding Generalization in Quantum Machine Learning with Margins

    Tak Hur and Daniel K. Park · ICML 2025 · Tutorial

  4. 2024

    Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning

    Tak Hur, I. Araujo, and Daniel K. Park · Physical Review A 110, 022411 · Tutorial

Learn by doing

Tutorials

Walk through code and ideas behind neural quantum embedding, quantum margins, and quantum convolutional networks.

Explore tutorials