email: yu_tong at berkeley dot edu
I am moving to Caltech as an IQIM Postdoctoral Scholar. 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 numerical analysis, quantum computing, computational physics, and computational chemistry.
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.
 Y. Dong, L. Lin, Y. Tong, Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices [arXiv:2204.05955]
 N. Abrahamsen, Y. Su, Y. Tong, N. Wiebe, Entanglement area law for 1D gauge theories and bosonic systems [arXiv:2203.16012]
 X. Wu, M. Lindsey, T. Zhou, Y. Tong, and L. Lin, Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting, Phys. Rev. B [doi] [arXiv:2003.00873]
 X. Wu, Z.-H. Cui, Y. Tong, M. Lindsey, G. K.-L. Chan, and L. Lin, Projected density matrix embedding theory with applications to the two-dimensional Hubbard model, J. Chem. Phys. [doi] [arXiv:1905.00886]