Yu Tong

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.

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.

Publications and preprints

[1] G. H. Low, Y. Su, Y. Tong, M. C. Tran, On the complexity of implementing Trotter steps [arXiv:2211.09133]

[2] H.-Y. Huang, Y. Tong, D. Fang, Y. Su, Learning many-body Hamiltonians with Heisenberg-limited scaling [arXiv:2210.03030] [QIP 2023 Talk]

[3] D. Fang, L. Lin, Y. Tong, Time-marching based quantum solvers for time-dependent linear differential equations [arXiv:2208.06941]

[4] 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]

[5] N. Abrahamsen, Y. Tong, N. Bao, Y. Su, N. Wiebe, Entanglement area law for 1D gauge theories and bosonic systems [arXiv:2203.16012] [QIP 2023 Talk]

[6] Y. Tong, V. V. Albert, J. R. McClean, J. Preskill, Y. Su, Provably accurate simulation of gauge theories and bosonic systems [arXiv:2110.06942] [QIP 2022 Talk]

[7] L. Lin and Y. Tong, Heisenberg-limited ground state energy estimation for early fault-tolerant quantum computers, PRX Quantum [doi] [arXiv:2102.11340]

[8] Y. Tong, D. An, N. Wiebe, L. Lin, Fast inversion, preconditioned quantum linear system solvers, and fast evaluation of matrix functions, Phys. Rev. A [doi] [arXiv:2008.13295]

[9] L. Lin and Y. Tong, Near-optimal ground state preparation, Quantum [doi] [arXiv:2002.12508] [QIP 2021 Talk]

[10] L. Lin and Y. Tong, Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems, Quantum [doi] [arXiv:1910.14596]

[11] L. Lin and Y. Tong, Low-rank representation of tensor network operators with long-range pairwise interactions, SIAM J. Sci. Comput. [doi] [arXiv:1909.02206]

[12] 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]

[13] 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]