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Zhen Qin |
Zhen Qin
I am currently on the academic job market and am seeking positions at the intersection of artificial intelligence, quantum computing, and information processing. I welcome inquiries and opportunities to discuss potential positions.
Hello, I am Zhen Qin, a MICDE Research Fellow jointly appointed in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of Michigan. I am fortunate to work closely with Professors Qing Qu and Yang Chen. I received my Ph.D. in Computer Science and Engineering from The Ohio State University, where I was privileged to be advised by Professor Zhihui Zhu.News and Updates
[Jan 2026] Our paper has been accepted at ICLR, presenting a theoretical analysis of in-context learning in non-stationary or time-varying regression problems.
[Jan 2026] Our paper has been accepted at PRA, establishing that a polynomial number of state copies is required to ensure bounded recovery error in the quantum state tomography of two-dimensional tensor network states.
[Jan 2026] Our paper has been accepted at TQE, investigating the optimal allocation of Pauli measurements in the low-rank quantum state tomography.
[Dec 2025] Our paper has been accepted at TSP, providing optimal error analysis of channel estimation for IRS-assisted MIMO systems.
[Aug 2025] Our paper has been accepted at npj Quantum Information, presenting a projected classical shadow method for quantum state tomography.
[Jul 2025] Our paper has been accepted at TPAMI, offering computational and statistical guarantees for tensor-on-tensor regression with tensor train decomposition.
[Jun 2025] Our paper has been released at arXiv, proposing a scalable factorization approach for high-order structured tensor recovery.
[Apr 2025] Our paper has been accepted at TSP, offering a theoretical analysis of the robust tensor train (TT) recovery problem and demonstrating that TT-format tensors can be robustly recovered even when up to half of the measurements are arbitrarily corrupted.
[Apr 2025] Honored to receive the 2025 CSE Graduate Research Award at Ohio State.
[Dec 2024] Our paper has been accepted at JMLR, offering a convergence guarantee for the factorization approach in arbitrary-order tensor train recovery.
[Oct 2024] Our paper has been released at arXiv, proving that a linear number of state copies is required to guarantee bounded recovery error of an matrix product operator state in the quantum state tomography, thereby improving the theoretical result in our TIT paper.
[Mar 2024] Our paper has been accepted at SPL, analyzing the linear converence rate of training the orthonormal deep linear neural networks.
[Jan 2024] Our paper has been accepted at TIT, demonstrating that a polynomial number of state copies is required to guarantee bounded recovery error of an matrix product operator state in the quantum state tomography.
[Jan 2024] A series of proportionate recursive least squares (PRLS) algorithms have been completed and accepted in the following papers: paper: PRLS, paper: L1-PRLS, paper: VSS-CR-PRLS and paper: Fast PRLS, exploring proportionate sparsity in the adaptive signal processing during my master's studies.
Select Publications
Z. Qin, J. Lukens, B. Kirby and Z. Zhu, ‘‘ Enhancing Quantum State Reconstruction with Structured Classical Shadows”, npj Quantum Information (npj QI), 2025.
Z. Qin and Z. Zhu, ‘‘ Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.
Z. Qin and Z. Zhu, ‘‘ Robust Low-rank Tensor Train Recovery”, IEEE Transactions on Signal Processing (TSP), 2025.
Z. Qin, C. Jameson, Z. Gong, M. B. Wakin and Z. Zhu, ‘‘ Quantum State Tomography for Matrix Product Density Operators”, IEEE Transactions on Information Theory (TIT), 2024.
Z. Qin, M. B. Wakin and Z. Zhu, ‘‘ Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery”, Journal of Machine Learning Research (JMLR), 2024.