Publications

Z. Qin ‘‘ Tensor Train Learning and Recovery: Theory, Algorithms, and Applications in Quantum State Tomography”, Ph.D. thesis, The Ohio State University 2025.

Z. Qin, J. Zhou, J. Jiang and Z. Zhu, ‘‘ On the Convergence of Gradient Descent on Learning Transformers with Residual Connections”, IEEE Signal Processing Letters (SPL), 2026.
Z. Qin, X. Tan and Z. Zhu, ‘‘Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks”, IEEE Signal Processing Letters (SPL), 2024.

Z. Qin, J. Jiang and Z. Zhu, ‘‘ Learning to Adapt: In-Context Learning Beyond Stationarity”, International Conference on Learning Representations (ICLR), 2026.

J. Jiang, Z. Qin and Z. Zhu, ‘‘ In-Context Learning for Non-Stationary MIMO Equalization”, arXiv preprint arXiv:2510.08711, 2025.

Z. Qin and Z. Zhu, ‘‘ Quantum State Tomography for Tensor Networks in Two Dimensions”, Physical Review A (PRA), 2026.
Z. Qin, C. Jameson, Z. Gong, M. B. Wakin and Z. Zhu, ‘‘ Optimal Allocation of Pauli Measurements for Low-rank Quantum State Tomography”, IEEE Transactions on Quantum Engineering (TQE), 2026.
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, 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.

A. Goldar, Z. Qin, Z. Zhu, Z. Gong and M. B. Wakin,‘‘ An Exponential Advantage for Adaptive Tomography of Structured States under Pauli Basis Measurements”, arXiv preprint arXiv.2604.26043, 2026.
Z. Qin, C. Jameson, A. Goldar, M. B. Wakin, Z. Gong and Z. Zhu, ‘‘ Sample-Efficient Quantum State Tomography for Structured Quantum States in One Dimension”, arXiv preprint arXiv.2410.02583, 2024. (Poster presented at QIP 2025.)
C. Jameson, Z. Qin, A. Goldar, M. B. Wakin, Z. Zhu, and Z. Gong, ‘‘ Optimal quantum state tomography with local informationally complete measurements”, arXiv preprint arXiv:2408.07115, 2024.
A. Lidiak, C. Jameson, Z. Qin, G. Tang, M. B. Wakin, Z. Zhu and Z. Gong, ‘‘ Quantum state tomography with tensor train cross approximations”, arXiv preprint arXiv:2207.06397, 2022.

Z. Qin and Z. Zhu, ‘‘ Optimal Error Analysis of Channel Estimation for IRS-assisted MIMO Systems”, IEEE Transactions on Signal Processing (TSP), 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, M. B. Wakin and Z. Zhu, ‘‘ Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery”, Journal of Machine Learning Research (JMLR), 2024.

X. Liang, Z. Qin, Z. Zhu and S. Li, ‘‘ Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2026.
L. Ding, Z. Qin, L. Jiang, J. Zhou and Z. Zhu, ‘‘ A Validation Approach to Over-parameterized Matrix and Image Recovery”, Conference on Parsimony and Learning (CPAL), 2025.
Z. Qin, A. Lidiak, Z. Gong, G. Tang, M. B. Wakin, and Z. Zhu, ‘‘ Error Analysis of Tensor Train Cross Approximation”, Neural Information Processing Systems (NeurIPS), 2022.
H. Yu, Z. Qin, and Z. Zhu, ‘‘ Learning approach for fast approximate matrix factorizations”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022.

X. Liang, Z. Qin, Z. Zhu and S. Li, ‘‘ Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery”, arXiv preprint arXiv:2509.10834, 2025.
Z. Qin, M. B. Wakin and Z. Zhu, ‘‘ A Scalable Factorization Approach for High-Order Structured Tensor Recovery”, arXiv preprint arXiv:2506.16032, 2025.

Y. Wang, Z. Qin, J. Tao, and Y. Xia, ‘‘ Variable step-size convex regularized PRLS algorithms”, Signal Processing (SP), 2024.
Z. Qin, J. Tao, L. Yang and M. Jiang, ‘‘ Proportionate recursive maximum correntropy criterion adaptive filtering algorithms and their performance analysis”, Digital Signal Processing (DSP), 2023.
Z. Qin, J. Tao, Y. Xia, and L. Yang, ‘‘ A proportionate RLS using norm regularization, performance analysis and its fast implementation”, Digital Signal Processing (DSP), 2022.
Z. Qin, J. Tao, and Y. Xia, ‘‘ A proportionate recursive least squares algorithm and its performance analysis”, IEEE Transactions on Circuits and Systems II: Express Briefs (TCASII), 2020.

Y. Wang, Z. Qin, J. Tao and M. Jiang, ‘‘ A Variable Step-Size l0-PRLS Algorithm and its Application in Sparse Channel Estimations”, IEEE 97th Vehicular Technology Conference (VTC), 2023.
Y. Wang, Z. Qin, J. Tao, and Y. Xia, ‘‘ Performance Analysis of PRLS-based Time-Varying Sparse System Identifications”, IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2022.
Z. Qin, J. Tao, L. An, S. Yao, and X. Han, ‘‘ Fast sparse RLS algorithms”, IEEE 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018.