Tongyang Li Assistant Professor

+86 (0)10 6276-6141


Room 103-1, Courtyard No.5, Jingyuan

Quantum Algorithms Design for Machine Learning and Optimization, Quantum Query Complexity, Quantum Simulation, Quantum Walks


Dr. Tongyang Li joined Peking University in July 2021 and is currently an assistant professor at Center on Frontiers of Computing Studies, Peking University. Previously he was a postdoctoral associate at the Center for Theoretical Physics, Massachusetts Institute of Technology. He received Master and Ph.D. degrees from the Department of Computer Science, University of Maryland in 2018 and 2020, respectively. He received Bachelor of Engineering from Institute for Interdisciplinary Information Sciences, Tsinghua University and Bachelor of Science from Department of Mathematical Sciences, Tsinghua University, both in 2015. Dr. Tongyang Li's research focuses on designing quantum algorithms for machine learning and optimization. In general, he is interested in better understanding about the power of quantum algorithms, including topics such as quantum query complexity, quantum simulation, and quantum walks. He was a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship.


■ (by contribution) Chenyi Zhang and Tongyang Li, Escape saddle points by a simple gradient-descent based algorithm, to appear in the 35th Annual Conference on Neural Information Processing Systems (NeurIPS 2021).
■ (by contribution) Chenyi Zhang*, Jiaqi Leng*, and Tongyang Li, Quantum algorithms for escaping from saddle points. Quantum, 5:529, 2021.

■ Andrew M. Childs, Shih-Han Hung, and Tongyang Li, Quantum query complexity with matrix-vector products. To appear in the 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). arXiv:2102.11349
■ Troy Lee, Tongyang Li, Miklos Santha, and Shengyu Zhang, On the cut dimension of a graph. To appear in the 2021 Computational Complexity Conference (CCC 2021). arXiv:2011.05085
■ (by contribution) Tongyang Li∗, Chunhao Wang∗, Shouvanik Chakrabarti, and Xiaodi Wu, Sublinear classical and quantum algorithms for general matrix games. To appear in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021). arXiv:2012.06519
■ (by contribution) Daochen Wang∗, Xuchen You∗, Tongyang Li, and Andrew M. Childs, Quantum exploration algorithms for multi-armed bandits. To appear in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021); also a contributed talk at the 4th Annual Conference on Quantum Techniques in Machine Learning (QTML 2020). arXiv:2007.07049
■ Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin, and Chunhao Wang, Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming. Proceedings of the 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020), Vol. 170, 23:1–23:15, Leibniz International Proceedings in Informatics, 2020. arXiv:1901.03254
■ Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, and Chunhao Wang, Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning. Proceedings of the 52nd Annual ACM Symposium on Theory of Computing (STOC 2020), 387–400, 2020; also a contributed talk at the 23rd Annual Conference on Quantum Information Processing (QIP 2020). arXiv:1910.06151
■ András Gilyén and Tongyang Li, Distributional property testing in a quantum world. Proceedings of the 11th Annual Conference on Innovations in Theoretical Computer Science (ITCS 2020), Vol. 151, 25:1–25:19, Leibniz International Proceedings in Informatics, 2020. arXiv:1902.00814
■ (by contribution) Shouvanik Chakrabarti∗, Yiming Huang∗, Tongyang Li, Soheil Feizi, and Xiaodi Wu, Quantum Wasserstein generative adversarial networks. Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 6778–6789, 2019. arXiv:1911.00111
■ (by contribution) Tongyang Li, Shouvanik Chakrabarti, and Xiaodi Wu, Sublinear quantum algorithms for training linear and kernel-based classifiers. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 3815–3824, 2019. arXiv:1904.02276
■ Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, and Xiaodi Wu, Quantum algorithms and lower bounds for convex optimization, Quantum, 4:221, 2020; also a contributed talk at the 22nd Annual Conference on Quantum Information Processing (QIP 2019). arXiv:1809.01731
Tongyang Li and Xiaodi Wu, Quantum query complexity of entropy estimation. IEEE Transactions on Information Theory Vol. 65, no. 5, 2899–2921, 2019. arXiv:1710.06025
■ Fernando G.S.L. Brandão, Amir Kalev, Tongyang Li, Cedric Y.-Y. Lin, Krysta M. Svore, and Xiaodi Wu, Quantum SDP Solvers: Large Speed-ups, Optimality, and Applications to Quantum Learning. Proceedings of the 46th International Colloquium on Automata, Languages and Programming (ICALP 2019), Vol. 132, 27:1–27:14, Leibniz International Proceedings in Informatics, 2019; also a contributed talk at the 22nd Annual Conference on Quantum Information Processing (QIP 2019). arXiv:1710.02581
■ Andrew M. Childs and Tongyang Li, Efficient simulation of sparse Markovian quantum dynamics. Quantum Information & Computation 17 (2017), no. 11-12, 901–947,arXiv:1611.05543
■ (by contribution) Tongyang Li, Lei Song, Yongcai Wang, and Haisheng Tan, On Target Counting by Sequential Snapshots of Binary Proximity Sensors. In Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN 2015), pp. 19-34.

Research Lab


Laboratory for Quantum Algorithms: Theory and Practice (QUARK Lab)



The Laboratory for Quantum Algorithms, Theory and Practice (QUARK Lab) was established by Dr. Tongyang Li in 2021. QUARK Lab studies algorithms on quantum computers, focusing on the advantages of solving machine learning, optimization, statistics, number theory, graph theory, and many other problems on quantum computers compared to classical counterparts. QUARK Lab also studies quantum algorithms on noisy, intermediate-scale (NISQ) quantum computers.