Thesis Information
Title: Quantum algorithms for machine learning and optimization
Adviser: Andrew M. Childs
Institution: University of Maryland
Graduation Date: May 2020
Contact Information
Email Candidate
Candidate Website
Candidate Bio:
Tongyang Li is a Ph.D. candidate at the Department of Computer Science, University of Maryland. 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 is a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship. His research focuses on designing quantum algorithms for machine learning and optimization.
Paper 1:
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.
Link to PDFPaper 2:
Shouvanik Chakrabarti, Andrew M. Childs, Tongyang Li, and Xiaodi Wu, Quantum algorithms and lower bounds for convex optimization. Contributed talk at the 22nd Annual Conference on Quantum Information Processing (QIP 2019).
Link to PDFPaper 3:
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; also a contributed talk at the 22nd Annual Conference on Quantum Information Processing (QIP 2019).
Link to PDFKeywords: Quantum computing, theoretical machine learning, optimization theory