Publications (30)
High-Probability Convergence Theory for Distributed Composite Optimization with Sub-Weibull Noises
Zhan Yu, Zhongjie Shi, Deming Yuan
With the rapid development of distributed optimization (DO) theory, the distributed stochastic gradient methods (DSGMs) occupy an important position. Although the theory of differe…
Efficient information recovery from Pauli noise via classical shadow
Yifei Chen, Zhan Yu, Chenghong Zhu +1
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields lik…
Generic Frameworks for Distributed Functional Optimization and Learning over Time-Varying Networks
Zhan Yu, Zhongjie Shi, Deming Yuan +1
In this paper, we establish a distributed functional optimization (DFO) theory over time-varying networks. The vast majority of existing distributed optimization theories are devel…
Power and limitations of single-qubit native quantum neural networks
Zhan Yu, Hongshun Yao, Mujin Li +1
Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have bee…
Simultaneous Estimation of Nonlinear Functionals of a Quantum State
Kean Chen, Qisheng Wang, Zhan Yu +1
We consider a fundamental task in quantum information theory, estimating the values of , , ..., for an…
Learning Arbitrary Lindbladians from Time Evolution
Zhili Chen, Zhan Yu
The paper presents an efficient algorithm for learning the full generator (Lindbladian) of a Markovian open quantum system from its time evolution, requiring only product Pauli pre…
Analysis of Lackadaisical Quantum Walks
Peter Høyer, Zhan Yu
The lackadaisical quantum walk is a quantum analogue of the lazy random walk obtained by adding a self-loop to each vertex in the graph. We analytically prove that lackadaisical qu…
Exploring experimental limit of deep quantum signal processing using a trapped-ion simulator
J. -T. Bu, Lei Zhang, Zhan Yu +12
Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building blo…
Distributed Stochastic Constrained Composite Optimization over Time-Varying Network with a Class of Communication Noise
Zhan Yu, Daniel W. C. Ho, Deming Yuan +1
This paper is concerned with distributed stochastic multi-agent constrained optimization problem over time-varying network with a class of communication noise. This paper considers…
Learning Theory of Distribution Regression with Neural Networks
Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classi…
Amortized Stabilizer Rényi Entropy of Quantum Dynamics
Chengkai Zhu, Yu-Ao Chen, Zanqiu Shen +3
Unraveling the secrets of how much nonstabilizerness a quantum dynamic can generate is crucial for harnessing the power of magic states, the essential resources for achieving quant…
Zeroth-Order Stochastic Block Coordinate Type Methods for Nonconvex Optimization
Zhan Yu, Daniel W. C. Ho
We study (constrained) nonconvex (composite) optimization problems where the decision variables vector can be split into blocks of variables. Random block projection is a popular t…
Theory of Decentralized Robust Kernel-Based Learning
Zhan Yu, Zhongjie Shi, Ding-Xuan Zhou
We propose a new decentralized robust kernel-based learning algorithm within the framework of reproducing kernel Hilbert spaces (RKHSs) by utilizing a networked system that can be…
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Zhan Yu, Qiuhao Chen, Yuling Jiao +4
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their expressive power in accomplishing machine learn…
Near-Optimal Learning of Local Lindbladians
Itai Arad, Zhili Chen, Naixu Guo +2
We study the problem of learning local Lindbladians from black-box access to the physical evolution, where the goal is to estimate all Hamiltonian and dissipative coefficients. For…
Quantum Phase Processing and its Applications in Estimating Phase and Entropies
Youle Wang, Lei Zhang, Zhan Yu +1
Quantum computing can provide speedups in solving many problems as the evolution of a quantum system is described by a unitary operator in an exponentially large Hilbert space. Suc…
Optimal quantum dataset for learning a unitary transformation
Zhan Yu, Xuanqiang Zhao, Benchi Zhao +1
Unitary transformations formulate the time evolution of quantum states. How to learn a unitary transformation efficiently is a fundamental problem in quantum machine learning. The…
Distributed Randomized Gradient-Free Mirror Descent Algorithm for Constrained Optimization
Zhan Yu, Daniel W. C. Ho, Deming Yuan
This paper is concerned with multi-agent optimization problem. A distributed randomized gradient-free mirror descent (DRGFMD) method is developed by introducing a randomized gradie…
Robust Kernel-based Distribution Regression
Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou
Regularization schemes for regression have been widely studied in learning theory and inverse problems. In this paper, we study distribution regression (DR) which involves two stag…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
Distributed Stochastic Block Coordinate Descent for Time-Varying Multi-Agent Optimization
Zhan Yu, Daniel W. C. Ho
In this paper, a class of large-scale distributed nonsmooth convex optimization problem over time-varying multi-agent network is investigated. Specifically, the decision space whic…
Quantum Transformer: Accelerating model inference via quantum linear algebra
Naixu Guo, Zhan Yu, Matthew Choi +5
Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transforme…
Distributed Constrained Optimization with Delayed Subgradient Information over Time-Varying Network under Adaptive Quantization
Jie Liu, Zhan Yu, Daniel W. C. Ho
In this paper, we consider a distributed constrained optimization problem with delayed subgradient information over the time-varying communication network, where each agent can onl…
Mathematical Mechanism on Dynamical System Algorithms of the Ising Model
Bowen Liu, Kaizhi Wang, Dongmei Xiao +1
Various combinatorial optimization NP-hard problems can be reduced to finding the minimizer of an Ising model, which is a discrete mathematical model. It is an intellectual challen…
A Co-Prime Blur Scheme for Data Security in Video Surveillance
Christopher Thorpe, Feng Li, Zijia Li +3
This paper presents a novel Coprime Blurred Pair (CBP) model for visual data-hiding for security in camera surveillance. While most previous approaches have focused on completely e…
Distributed Stochastic Optimization under Heavy-Tailed Noise: A Federated Mirror Descent Approach with High Probability Convergence
Zhan Yu, Lan Liao, Deming Yuan +2
We study the distributed stochastic optimization (DSO) problem under a heavy-tailed noise condition by utilizing a multi-agent system. Despite the extensive research on DSO algorit…
Distributed Gradient Descent for Functional Learning
Zhan Yu, Jun Fan, Zhongjie Shi +1
In recent years, different types of distributed and parallel learning schemes have received increasing attention for their strong advantages in handling large-scale data informatio…
On estimating operator norm distance, with optimal trace distance estimation when one state is pure
Yupan Liu, Qisheng Wang, Zhan Yu
We investigate the computational complexity of estimating the operator norm distance , defined via the operator norm , g…
Estimates on Learning Rates for Multi-Penalty Distribution Regression
Zhan Yu, Daniel W. C. Ho
This paper is concerned with functional learning by utilizing two-stage sampled distribution regression. We study a multi-penalty regularization algorithm for distribution regressi…
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo +6
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum compute…