15 citations · 28 across the 26 of their papers we have counts for
28 papers
Generalization of Transformer-Based Neural Quantum States via In-Context Learning
Zhen Qin, Qing Qu, Alfred O. Hero
Neural quantum states based on modern deep learning architectures have emerged as powerful representations for quantum many-body systems. In particular, Transformer-based neural qu…
From Barren Plateaus to SPSA Optimization in Variational Quantum Eigensolvers
Zhen Qin
The barren plateau (BP) phenomenon poses a fundamental challenge to the trainability of variational quantum eigensolvers (VQEs) by causing exponentially vanishing gradients as the…
A Unified Framework for Sample Complexity of Structured Quantum State Tomography under Noisy Observations
Zhen Qin
Quantum state tomography (QST) has attracted considerable attention due to its fundamental role in quantum information processing. In this paper, we develop a unified theoretical f…
Structured Factorization Approaches for Quantum State Tomography
Zhen Qin, Joseph M. Lukens, Brian T. Kirby +1
Since the complexity of quantum state tomography (QST) scales exponentially with system size, exploiting priors such as low-rankness, tensor-network structures, and neural-network…
Structured Adaptive Tensor Prediction for Streaming Data
Zhen Qin, Yang Chen
Matrix-valued time series arise in a wide range of applications, such as spatio-temporal data from medical imaging and geophysics. Existing methods are mainly designed for static s…
Geometric Analysis of Variational Quantum Eigensolver
Zhen Qin
The Variational Quantum Eigensolver (VQE) is a fundamental algorithm in quantum computing, yet a coherent geometric characterization of VQE remains missing due to fragmented analys…
Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review
Zhen Qin, Michael B. Wakin, Zhihui Zhu
Quantum state tomography (QST) is a fundamental task in quantum information science that aims to reconstruct unknown quantum states from measurement data. However, the exponential…
Learning to Adapt: In-Context Learning Beyond Stationarity
Zhen Qin, Jiachen Jiang, Zhihui Zhu
Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key capability underlying their s…
In-Context Learning for Non-Stationary MIMO Equalization
Jiachen Jiang, Zhen Qin, Zhihui Zhu
Channel equalization is fundamental for mitigating distortions such as frequency-selective fading and inter-symbol interference. Unlike standard supervised learning approaches that…
Quantum State Tomography for Tensor Networks in Two Dimensions
Zhen Qin, Zhihui Zhu
Recent work has shown that for one-dimensional quantum states that can be effectively approximated by matrix product operators (MPOs), a polynomial number of copies of the state su…
Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery
Xiao Liang, Zhen Qin, Zhihui Zhu +1
This paper presents a geometric analysis of the simultaneous blind deconvolution and phase retrieval (BDPR) problem via a structured low-rank tensor recovery framework. Due to the…
A Scalable Factorization Approach for High-Order Structured Tensor Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu
Tensor decompositions, which represent an -order tensor using approximately factors of much smaller dimensions, can significantly reduce the number of parameters. This is pa…
On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
Zhen Qin, Jinxin Zhou, Jiachen Jiang +1
Transformer models have emerged as fundamental tools across various scientific and engineering disciplines, owing to their outstanding performance in diverse applications. Despite…
Enhancing Quantum State Reconstruction with Structured Classical Shadows
Zhen Qin, Joseph M. Lukens, Brian T. Kirby +1
Quantum state tomography (QST) remains the prevailing method for benchmarking and verifying quantum devices; however, its application to large quantum systems is rendered impractic…
Optimal Error Analysis of Channel Estimation for IRS-assisted MIMO Systems
Zhen Qin, Zhihui Zhu
As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted…
Optimal Allocation of Pauli Measurements for Low-rank Quantum State Tomography
Zhen Qin, Casey Jameson, Zhexuan Gong +2
The process of reconstructing quantum states from experimental measurements, accomplished through quantum state tomography (QST), plays a crucial role in verifying and benchmarking…
Robust Low-rank Tensor Train Recovery
Zhen Qin, Zhihui Zhu
Tensor train (TT) decomposition represents an -order tensor using matrices (i.e., factors) of small dimensions, achieved through products among these factors. Due to its…
Sample-Efficient Quantum State Tomography for Structured Quantum States in One Dimension
Zhen Qin, Casey Jameson, Alireza Goldar +3
While quantum state tomography (QST) remains the gold standard for benchmarking and verifying quantum devices, it requires an exponentially large number of measurements and classic…
Optimal quantum state tomography with local informationally complete measurements
Casey Jameson, Zhen Qin, Alireza Goldar +3
Quantum state tomography (QST) remains the gold standard for benchmarking and verification of near-term quantum devices. While QST for a generic quantum many-body state requires an…
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition
Zhen Qin, Zhihui Zhu
Recently, a tensor-on-tensor (ToT) regression model has been proposed to generalize tensor recovery, encompassing scenarios like scalar-on-tensor regression and tensor-on-vector re…
Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery
Zhen Qin, Michael B. Wakin, Zhihui Zhu
In this paper, we provide the first convergence guarantee for the factorization approach. Specifically, to avoid the scaling ambiguity and to facilitate theoretical analysis, we op…
Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
Zhen Qin, Xuwei Tan, Zhihui Zhu
Enforcing orthonormal or isometric property for the weight matrices has been shown to enhance the training of deep neural networks by mitigating gradient exploding/vanishing and in…
Quantum State Tomography for Matrix Product Density Operators
Zhen Qin, Casey Jameson, Zhexuan Gong +2
The reconstruction of quantum states from experimental measurements, often achieved using quantum state tomography (QST), is crucial for the verification and benchmarking of quantu…
Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications
Zhen Qin
Sparse structures are widely recognized and utilized in channel estimation. Two typical mechanisms, namely proportionate updating (PU) and zero-attracting (ZA) techniques, achieve…