Tongyang Li

tongyang-li's picture
Graduate Student (2015-2020)

Tongyang Li received his PhD in Computer Science from the University of Maryland in 2020.  He received B.E. from Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from Department of Mathematical Sciences, Tsinghua University, both in 2015; he also received a Master degree from Department of Computer Science, University of Maryland in 2018. He was a recipient of the IBM Ph.D. Fellowship and the NSF QISE-NET Triplet Award. His research focuses on designing quantum algorithms for optimization and machine learning.  Tongyang held a QuICS Lanczos Graduate Fellowship 2015-2017. He is a postdoc at MIT.

Publications

2023

S. Chakrabarti, Childs, A. M., Hung, S. - H., Li, T., Wang, C., and Wu, X., “Quantum algorithm for estimating volumes of convex bodies”, ACM Transactions on Quantum Computing, vol. 4, 2023.

2022

Q. Zhao, Zhou, Y., Shaw, A. F., Li, T., and Childs, A. M., “Hamiltonian simulation with random inputs”, Phys. Rev. Lett. 129, 270502, vol. 129, no. 270502, 2022.
A. M. Childs, Leng, J., Li, T., Liu, J. - P., and Zhang, C., “Quantum simulation of real-space dynamics”, Quantum, vol. 6, p. 860, 2022.

2021

C. Zhang, Leng, J., and Li, T., “Quantum Algorithms for Escaping from Saddle Points”, Quantum, vol. 5, no. 529, 2021.
A. M. Childs, Hung, S. - H., and Li, T., “Quantum query complexity with matrix-vector products”, Proceedings of the 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021), Leibniz International Proceedings in Informatics, vol. 198, pp. 55:1-55:19, 2021.
D. Wang, You, X., Li, T., and Childs, A. M., “Quantum exploration algorithms for multi-armed bandits”, Proceedings of the 35th Conference on Artificial Intelligence (AAAI 2021), vol. 35, no. 11, pp. 10102-10110, 2021.

2020

N. - H. Chia, Gilyen, A., Li, T., Lin, H. - H., Tang, E., and Wang, C., “Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning”, to appear in Proceedings of STOC 2020, 2020.
S. Chakrabarti, Childs, A. M., Li, T., and Wu, X., “Quantum algorithms and lower bounds for convex optimization”, Quantum, vol. 4, no. 221, 2020.
T. Li, Wang, C., Chakrabarti, S., and Wu, X., “Sublinear classical and quantum algorithms for general matrix games”, To appear in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), 2020.
A. Gilyen and Li, T., “Distributional property testing in a quantum world”, Proceedings of ITCS 2020, vol. 25, no. 19, pp. 1-25, 2020.

2019

S. Chakrabarti, Huang, Y., Li, T., Feizi, S., and Wu, X., “Quantum Wasserstein Generative Adversarial Networks”, Advances in Neural Information Processing Systems (NIPS), vol. 32, 2019.
T. Li and Wu, X., “Quantum query complexity of entropy estimation”, IEEE Transactions on Information Theory, vol. 65, no. 5, pp. 2899-2921, 2019.
T. Li, Chakrabarti, S., and Wu, X., “Sublinear quantum algorithms for training linear and kernel-based classifiers”, Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR , vol. 97, pp. 3815-3824, 2019.
N. - H. Chia, Li, T., Lin, H. - H., and Wang, C., “Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches”, 2019.

2018

F. G. S. L. Brandão, Kalev, A., Li, T., Lin, C. Yen- Yu, Svore, K. M., and Wu, X., “Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning”, To appear at the 46th International Colloquium on Automata, Languages and Programming (ICALP 2019), 2018.

2017

F. G. S. L. Brandão, Kalev, A., Li, T., Lin, C. Yen- Yu, Svore, K. M., and Wu, X., “Exponential Quantum Speed-ups for Semidefinite Programming with Applications to Quantum Learning”, 2017.
A. M. Childs and Li, T., “Efficient simulation of sparse Markovian quantum dynamics”, Quantum Information and Computation, vol. 17, pp. 901-947, 2017.