Publications

Machine Learning

• Z. Qin, J. Zhou and Z. Zhu, ‘‘ On the Convergence of Gradient Descent on Learning Transformers with Residual Connections”, arXiv preprint arXiv:2506.05249, 2025.

• Z. Qin, X. Tan and Z. Zhu, ‘‘Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks”, IEEE Signal Processing Letters (SPL), 2024.

Quantum Information and Tomography

• Z. Qin, J. Lukens, B. Kirby and Z. Zhu, ‘‘ Enhancing Quantum State Reconstruction with Structured Classical Shadows”, arXiv preprint arXiv:2501.03144, 2025.

• Z. Qin, C. Jameson, Z. Gong, M. B. Wakin and Z. Zhu, ‘‘ Optimal Allocation of Pauli Measurements for Low-rank Quantum State Tomography”, arXiv preprint arXiv:2411.04452, 2024.

• Z. Qin, C. Jameson, A. Goldar, Z. Gong, M. B. Wakin and Z. Zhu, ‘‘ Sample-Efficient Quantum State Tomography for Structured Quantum States in One Dimension”, arXiv preprint arXiv.2410.02583, 2024.

• 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.

• 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. 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.

Matrix and Tensor Learning

• Z. Qin, M. B. Wakin and Z. Zhu, ‘‘ A Scalable Factorization Approach for High-Order Structured Tensor Recovery”, arXiv preprint arXiv:2506.16032, 2025.

• Z. Qin and Z. Zhu, ‘‘ Robust Low-rank Tensor Train Recovery”, IEEE Transactions on Signal Processing (TSP), 2025.

• 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, M. B. Wakin and Z. Zhu, ‘‘ Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery”, Journal of Machine Learning Research (JMLR), 2024.

• Z. Qin and Z. Zhu, ‘‘ Optimal Error Analysis of Channel Estimation for IRS-assisted MIMO Systems”, arXiv preprint arXiv:2412.16827, 2024.

• Z. Qin and Z. Zhu, ‘‘ Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition”, arXiv preprint arXiv:2406.06002, 2024.

• 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.

Adaptive Signal Processing

• Y. Wang, Z. Qin, J. Tao, and Y. Xia, ‘‘ Variable step-size convex regularized PRLS algorithms”, Signal Processing (SP), 2024.

• 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.

• 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.

• 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, 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.

• 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.

Underwater Acoustic Communications

• Z. Qin, ‘‘ Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications”, arXiv preprint arXiv:2304.11838, 2023.

• Y. Zhuang, J. Tao, Z. Qin, and M. Jiang, ‘‘ Enhanced MSER Adaptive Equalization for Single-Carrier MIMO Underwater Acoustic Communications”, MTS/IEEE OCEANS Conference (OCEANS), 2022.

• Z. Qin, J. Tao, F. Qu and Y. Qiao, ‘‘ Adaptive equalization based on dynamic compressive sensing for single-carrier multiple-input multiple-output underwater acoustic communications”, The Journal of the Acoustical Society of America (JASA), 2022.

• Y. Wang, Z. Qin, J. Tao, F. Tong and Y. Qiao, ‘‘ Sparse Adaptive Channel Estimation based on l0-PRLS Algorithm for Underwater Acoustic Communications”, MTS/IEEE OCEANS Conference (OCEANS), 2022.

• Z. Qin, J. Tao, and X. Han, ‘‘ Sparse direct adaptive equalization based on proportionate recursive least squares algorithm for multiple-input multiple-output underwater acoustic communications”, The Journal of the Acoustical Society of America (JASA), 2020.

• Z. Qin, J. Tao, and X. Han, ‘‘ Dynamic compressive sensing based adaptive equalization for underwater acoustic communications”, MTS/IEEE Global OCEANS Conference (OCEANS), 2020.

• Z. Qin, J. Tao, F. Tong, H. Zhang, and F. Qu, ‘‘ A fast proportionate RLS adaptive equalization for underwater acoustic communications”, MTS/IEEE OCEANS Conference (OCEANS), 2019.

• Z. Qin, J. Tao, X. Wang, X. Luo, and X. Han, ‘‘ Direct adaptive equalization based on fast sparse recursive least squares algorithms for multiple-input multiple-output underwater acoustic communications”, The Journal of the Acoustical Society of America (JASA), 2019.