Yu Tong
email: yu.tong at duke dot edu
office: Physics Building 217
I am an Assistant Professor at Duke Math and ECE. I obtained my B.S. degree in Computational Mathematics from Peking University in 2017, and Ph.D. in Applied Mathematics from UC Berkeley in 2022 advised by Lin Lin. I was an IQIM Postdoctoral Scholar at Caltech working with John Preskill and Garnet Chan from 2022 to 2024. I am broadly interested in quantum algorithms, quantum learning theory, and numerical and analytic methods for quantum many-body problems.
Research areas
Quantum algorithms: Quantum computers are naturally suited to solve problems arising in quantum chemistry, for which classical algorithms suffer from high computational cost and low accuracy. I am interested in developing quantum algorithms to solve problems such as estimting the ground energy, Green's function, etc., as well as addressing problems in practical implementations on near-term devices.
Tensor network methods: Tensor networks provide us with the basic tools to understand quantum systems. They also offer useful computational methods in solving quantum chemistry and quantum physics problems. I am interested in both theoretical analysis of existing tensor network algorithms and the development of new ones.
Quantum embedding methods: Given the prohibitive computational cost of dealing with a quantum system of large size on a classical computer, a natural idea is to decompose the system into smaller subsystems and solve for each subsystem. The interaction between a subsystem and the environment leads to many interesting computational tasks.
Quantum learning theory: There are many scenarios in which one would want to extract classical information from a quantum system. In quantum metrology and quantum sensing one may want to better understand a quantum system, or use it to measure some quantities to high precision. One may also wish to characterize properties of a quantum system, such as conservation laws and topological order, using limited measurement data, in which case machine learning can provide a significant advantage.
Recent sevice
Program Committee Member for QCTIP 2023, TQC 2023, QCTIP 2025, QSIM 2025.
Editor of Quantum