Hsin-Yuan (Robert) Huang
Hsin-Yuan (Robert) Huang
Assistant Professor of Theoretical Physics; William H. Hurt Scholar
B.S., National Taiwan University, 2018; Ph.D., California Institute of Technology, 2024. Visiting Associate, Caltech 2024-25; Assistant Professor, 2025-, Hurt Scholar, 2025-.
Research Interest: Quantum Information (theory)
Overview
I am Hsin-Yuan Huang (黃信元, pronounced "Shin Yuan Huan"). I also go by Robert.
My research aims to build a rigorous foundation for understanding how scientists, machines, and future quantum computers can learn and discover new phenomena governing our quantum-mechanical universe (molecules, materials, pharmaceutics, exotic quantum matter, engineered quantum devices, etc.).
I leverage quantum information theory, quantum many-body physics, learning theory, and complexity theory to formalize and explore new questions in the following directions:
- When can quantum machines learn and predict better than classical machines?
- How to accelerate/automate the development of quantum and physical sciences?
- What physical phenomena can classical vs quantum machines learn and discover?
My ultimate goal is to build quantum machines capable of discovering new facets of our universe beyond the capabilities of humans and classical machines.
Selected Awards
- Endowed Early Career Professorship, William H. Hurt Scholar, California Institute of Technology, 2025
- Research Awards Frontiers of Science Award in Condensed Matter Physics, 2025; "Predicting Many Properties of a Quantum System from Very Few Measurements"
- American Physical Society DQI Best Thesis Award, 2025
- Broadcom Innovation Fund, 2024
- Milton and Francis Clauser Doctoral Prize, 2024 (awarded to a single Caltech Ph.D. graduate whose thesis exhibits the highest originality)
- Ben P. C. Chou Doctoral Prize in Information Science and Technology, 2024
- Google Ph.D. Fellowship, 2021 - 2023
- Boeing Quantum Creators Prize, 2021
Selected Awards
- Endowed Early Career Professorship, William H. Hurt Scholar, California Institute of Technology, 2025
- Research Awards Frontiers of Science Award in Condensed Matter Physics, 2025; "Predicting Many Properties of a Quantum System from Very Few Measurements"
- American Physical Society DQI Best Thesis Award, 2025
- Broadcom Innovation Fund, 2024
- Milton and Francis Clauser Doctoral Prize, 2024 (awarded to a single Caltech Ph.D. graduate whose thesis exhibits the highest originality)
- Ben P. C. Chou Doctoral Prize in Information Science and Technology, 2024
- Google Ph.D. Fellowship, 2021 - 2023
- Boeing Quantum Creators Prize, 2021
Related News
Read more newsRelated Courses
Ph 121 abc. Computational Physics Lab.
6 units (0-6-0); first, second, third terms, 2025-26.
Many of the recent advances in physics are attributed to progress in computational power. In the advanced computational lab, students will hone their computational skills by working through projects inspired by junior level classes (such as classical mechanics and E, statistical mechanics, quantum mechanics and quantum many-body physics). This course will primarily be in Python and Mathematica. This course is offered pass/fail.
Instructors: Simmons-Duffin, Huang
Instructors: Simmons-Duffin, Huang
Ph 220. Quantum Learning Theory.
9 units (3-0-6); first term, 2025-26.
Prerequisites: Ph 125 ab or equivalent.
This course covers quantum learning theory, a contemporary field at the intersection of quantum mechanics, quantum computing, statistical learning theory, and machine learning. The fundamental questions explored include: how to efficiently learn quantum many-body systems? When can quantum machines learn and predict better than classical machines? What physical phenomena can quantum machines learn and discover? The course aims to develop rigorous theoretical foundations for understanding how scientists, machines, and future quantum computers can learn and discover new phenomena in our quantum-mechanical universe.
Instructor: Huang
Instructor: Huang