Preprints

• (by contribution) Yizhou Liu, Weijie J. Su, and Tongyang Li. On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks. In submission. Supplementary Information

• Xinyi Chen, Elad Hazan, Tongyang Li, Zhou Lu, Xinzhao Wang, Rui Yang, Adaptive Online Learning of Quantum States. In submission.

• (by contribution) Yan Zhu, Ge Bai, Yuexuan Wang, Tongyang Li, and Giulio Chiribella, Quantum autoencoders for communication-efficient quantum cloud computing. In submission.

• Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung, Tongyang Li, Chunhao Wang, and Xiaodi Wu, Quantum algorithm for estimating volumes of convex bodies. In submission. Single-track contributed talk at the 23rd Annual Conference on Quantum Information Processing (QIP 2020; 17 among 283 submissions).

Publications

(Unless marked as "by contribution", the authors of papers are listed alphabetically. * marks equal contribution.)

• (by contribution) Qi Zhao, You Zhou, Alexander F. Shaw, Tongyang Li, and Andrew M. Childs, Hamiltonian simulation with random inputs. Accepted by Physical Review Letters; also a contributed talk at the 25th Conference on Quantum Information Processing (QIP 2022).

• (by contribution) Zongqi Wan, Zhijie Zhang, Tongyang Li, Jialin Zhang, Xiaoming Sun, Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets. Accepted by the 37th AAAI Conference on Artificial Intelligence (AAAI 2023).

• (by contribution) Xiao-Ming Zhang, Tongyang Li, and Xiao Yuan, Quantum State Preparation with Optimal Circuit Depth: Implementations and Applications. Accepted by Physical Review Letters.

• Andrew M. Childs, Jiaqi Leng, Tongyang Li, Jin-Peng Liu, and Chenyi Zhang, Quantum simulation of real-space dynamics. Quantum, 6:860, 2022.

• Tongyang Li and Ruizhe Zhang, Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits. Accepted by the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

• Andrew M. Childs, Tongyang Li, Jin-Peng Liu, Chunhao Wang, and Ruizhe Zhang, Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants. Accepted by the 36th Conference on Neural Information Processing Systems (NeurIPS 2022); also accepted as a talk at the 26th Conference on Quantum Information Processing (QIP 2023).

• (by contribution) Chenyi Zhang and Tongyang Li, Escape saddle points by a simple gradient-descent based algorithm. Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 8545-8556, 2021.

• (by contribution) Chenyi Zhang*, Jiaqi Leng*, and Tongyang Li, Quantum algorithms for escaping from saddle points. Quantum, 5:529, 2021; also a contributed talk at the 24th Conference on Quantum Information Processing (QIP 2021).

• Andrew M. Childs, Shih-Han Hung, and Tongyang Li, Quantum query complexity with matrix-vector products. Proceedings of the 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021), Vol. 198, 55:1-55:19, Leibniz International Proceedings in Informatics, 2021.

• Troy Lee, Tongyang Li, Miklos Santha, and Shengyu Zhang, On the cut dimension of a graph. Proceedings of the 36th Computational Complexity Conference (CCC 2021), Vol. 200, 15:1-15:35, Leibniz International Proceedings in Informatics, 2021.

• (by contribution) Tongyang Li*, Chunhao Wang*, Shouvanik Chakrabarti, and Xiaodi Wu, Sublinear classical and quantum algorithms for general matrix games. Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 35(10): 8465-8473, 2021.

• (by contribution) Daochen Wang*, Xuchen You*, Tongyang Li, and Andrew M. Childs, Quantum exploration algorithms for multi-armed bandits. Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 35(11): 10102-10110, 2021; also a contributed talk at the 4th Annual Conference on Quantum Techniques in Machine Learning (QTML 2020).

• 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.

• 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).

• 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.

• (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.

• (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.

• 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).

• Tongyang Li and Xiaodi Wu, Quantum query complexity of entropy estimation. IEEE Transactions on Information Theory Vol. 65, no. 5, 2899–2921, 2019.

• 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).

• Andrew M. Childs and Tongyang Li, Efficient simulation of sparse Markovian quantum dynamics. Quantum Information & Computation 17 (2017), no. 11-12, 901–947.