Hsin-Yuan Huang (Robert) is a research scientist at Google Quantum AI. He obtained his PhD from Caltech in 2023, advised by John Preskill and Thomas Vidick. His research aims to build a rigorous foundation for modeling how scientists, machines, and future quantum computers learn about our inherently quantum-mechanical universe (molecules, materials, pharmaceutics, exotic quantum matter, engineered quantum devices, etc.). With the rigorous foundation, he seeks to discover new algorithmic tools that enhance one’s ability to make predictions about the quantum world.
His notable works include classical shadow tomography for learning large-scale quantum systems, provably efficient machine learning algorithms for solving quantum many-body problems, and quantum advantages in learning from experiments. He has been awarded a Google PhD fellowship, the Quantum Creator Prize, MediaTek research young scholarship, and the Kortschak Scholarship.