Yu Tong

email: yu_tong at berkeley dot edu

I am an IQIM Postdoctoral Scholar at Caltech mentored by John Preskill and Garnet Chan. I obtained my Ph.D. in Applied Mathematics from UC Berkeley in 2022, advised by Lin Lin, and my B.S. degree in computational mathematics from Peking University in 2017. I am broadly interested in quantum algorithms, learning from quantum systems, 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.

Learning from quantum systems: 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 for QCTIP 2023 and TQC 2023.

Publications and preprints

(for most up to date information, see Google Scholar)

Unpublished notes

Note on query complexity lower bound for phase estimation under circuit depth constraint