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Date:  Fri, October 17, 2025
Time:  10:30am
Location:  Holmes Hall 389
Speaker:  Dr. Zhihui Zhu, Ohio State University

(hosted by Prof. Anders Host-Madsen (ahm@hawaii.edu), College of Engineering, ECE Department)

ECE Graduate Students: This will count towards your seminar credit.

Abstract

In the foreseeable future, the advent of large-scale error-corrected quantum computers will open new frontiers in both fundamental quantum science and practical applications. Yet the inherent complexity of these systems also poses challenges in precise control and accurate characterization of these systems. In this talk, we will present our recent work in understanding both the statistical and computational aspects of estimating (or learning) properties of quantum systems. While the sample complexity and runtime for estimating general quantum states scale exponentially with system size (i.e., the number of qubits), we will show how these requirements can be reduced to polynomial scaling for structured families of states, including matrix product states/operators and projected entangled-pair states/operators. We will also discuss how the design of measurement schemes impacts sample complexity and opportunities for more efficient characterization of large-scale quantum systems.

Biography

Zhihui Zhu is currently an Assistant Professor with the Department of Computer Science and Engineering at the Ohio State University. He was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Denver from 2020-2022 and a PostDoctoral Fellow with the Mathematical Institute for Data Science, Johns Hopkins University, from 2018 to 2019. He received his B.Eng. degree in communications engineering in 2012 from Zhejiang University of Technology (Jianxing Honors College), and his Ph.D. degree in electrical engineering in 2017 from the Colorado School of Mines. He was the recipient of the Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (ORAU). He is an elected member of Machine Learning for Signal Processing (MLSP) TC, an Action Editor of the Transactions on Machine Learning Research, and an Area Chair for ICML, ICLR, and NeurIPS.