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)Y. Zhan, A. Elben, H.-Y. Huang, Y. Tong, Learning conservation laws in unknown quantum dynamics [arXiv:2309.00774]
Z. Ding, Y. Dong, Y. Tong, L. Lin, Robust ground-state energy estimation under depolarizing noise [arXiv:2307.11257]
H. Li, Y. Tong, H. Ni, T. Gefen, L. Ying, Heisenberg-limited Hamiltonian learning for interacting bosons [arXiv:2307.04690]
G. H. Low, Y. Su, Y. Tong, M. C. Tran, Complexity of implementing Trotter steps, PRX Quantum [doi] [arXiv:2211.09133]
H.-Y. Huang, Y. Tong, D. Fang, Y. Su, Learning many-body Hamiltonians with Heisenberg-limited scaling, Phys. Rev. Lett. [doi] [arXiv:2210.03030] [QIP 2023 Talk]
S. Lee, J. Lee, H. Zhai, Y. Tong, A.M. Dalzell, A. Kumar, P. Helms, J. Gray, Z. Cui, W. Liu, M. Kastoryano, R. Babbush, J. Preskill, D. R Reichman, E. T Campbell, E. F Valeev, L. Lin, G. K.-L. Chan Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry, Nature Comm. [doi] [arXiv:2208.02199]
D. Fang, L. Lin, Y. Tong, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum [doi] [arXiv:2208.06941]
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, PRX Quantum [doi] [arXiv:2204.05955]
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]
Y. Tong, V. V. Albert, J. R. McClean, J. Preskill, Y. Su, Provably accurate simulation of gauge theories and bosonic systems , Quantum [doi] [arXiv:2110.06942] [QIP 2022 Talk]
L. Lin and Y. Tong, Heisenberg-limited ground state energy estimation for early fault-tolerant quantum computers, PRX Quantum [doi] [arXiv:2102.11340]
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]
L. Lin and Y. Tong, Near-optimal ground state preparation, Quantum [doi] [arXiv:2002.12508] [QIP 2021 Talk]
L. Lin and Y. Tong, Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems, Quantum [doi] [arXiv:1910.14596]
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]
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]