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.
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.
Program Committee for QCTIP 2023 and TQC 2023.
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]
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]
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]
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]