Publications

Ph.D. Thesis

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

Quantum Computing and Quantum Information

Journal

Z. Qin, ‘‘ Geometric Analysis of Variational Quantum Eigensolver”, Journal of Physics A: Mathematical and Theoretical (JPhysA), 2026.
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.

Preprint

A. Goldar, Z. Qin, Z. Zhu, Z. Gong and M. B. Wakin,‘‘ Adaptive Local Measurements For Quantum Learning Can Exponentially Outperform Nonadaptive Global Measurements”, arXiv preprint arXiv.2610.38922, 2026.
Z. Qin, Q. Qu and A. O. Hero III ‘‘ Generalization of Transformer-Based Neural Quantum States via In-Context Learning”, arXiv preprint arXiv:2610.03463, 2026.
Z. Qin, ‘‘ From Barren Plateaus to SPSA Optimization in Variational Quantum Eigensolvers”, arXiv preprint arXiv:2608.09810, 2026.
Z. Qin, ‘‘ A Unified Framework for Sample Complexity of Structured Quantum State Tomography under Noisy Observations”, arXiv preprint arXiv:2608.05526, 2026.
Z. Qin, J. Lukens, B. Kirby and Z. Zhu, ‘‘ Structured Factorization Approaches for Quantum State Tomography”, arXiv preprint arXiv:2607.01608, 2026.
Z. Qin, M. B. Wakin and Z. Zhu, ‘‘ Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review”, arXiv preprint arXiv:2605.27191, 2026.
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.

Optimization and Machine Learning

Journal

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

Conference

J. Jiang, Z. Qin and Z. Zhu, ‘‘ In-Context Learning for Non-Stationary MIMO Equalization”, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2026.
Z. Qin, J. Jiang and Z. Zhu, ‘‘ Learning to Adapt: In-Context Learning Beyond Stationarity”, International Conference on Learning Representations (ICLR), 2026.
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.

Preprint

Z. Qin and Y. Chen, ‘‘ Structured Adaptive Tensor Prediction for Streaming Data”, arXiv preprint arXiv:2606.10085, 2026.
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.

Adaptive Signal Processing

Journal

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.

Conference

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.

Underwater Acoustic Communications