4 citations · 5 across the 9 of their papers we have counts for
9 papers
Overcoming the Lamb Shift in System-Bath Interaction Models via KMS Detailed Balance: High-Accuracy Thermalization with Time-Bounded Interactions
Hongrui Chen, Zhiyan Ding, Ruizhe Zhang
We investigate quantum thermal state preparation algorithms based on system-bath interactions and uncover a surprising phenomenon in the weak-coupling regime. We rigorously prove t…
Thermal expectation estimation via single-trajectory Gibbs sampling with non-destructive measurements
Hongrui Chen, Jiaqing Jiang, Bowen Li +1
Estimating thermal expectation values of quantum many-body systems is a central challenge in physics, chemistry, and materials science. Standard quantum Gibbs sampling protocols ad…
Convergence of the Cumulant Expansion and Polynomial-Time Algorithm for Weakly Interacting Fermions
Hongrui Chen, Cambyse Rouzé, Jielun Chen +6
We propose a randomized algorithm to compute the log-partition function of weakly interacting fermions with polynomial runtime in both the system size and precision. Although weakl…
Inference for Similarity and Alignability between Noisy High-Dimensional Datasets
Hongrui Chen, Rong Ma
The rapid growth of high-dimensional datasets across a wide range of scientific domains has created an urgent need for new statistical methods to compare distributions with underly…
A Randomized Method for Simulating Lindblad Equations and Thermal State Preparation
Hongrui Chen, Bowen Li, Jianfeng Lu +1
We study a qDRIFT-type randomized method to simulate Lindblad dynamics by decomposing its generator into an ensemble of Lindbladians, $\mathcal{L} = \sum_{a \in \mathcal{A}} \mathc…
Convergence Analysis of Discrete Diffusion Model: Exact Implementation through Uniformization
Hongrui Chen, Lexing Ying
Diffusion models have achieved huge empirical success in data generation tasks. Recently, some efforts have been made to adapt the framework of diffusion models to discrete state s…
The Learnability of Reproducing Kernel Hilbert Spaces
Hongrui Chen, Jihao Long, Lei Wu
In this work, we analyze the learnability of reproducing kernel Hilbert spaces (RKHS) under the norm, which is critical for understanding the performance of kernel metho…
A duality framework for analyzing random feature and two-layer neural networks
Hongrui Chen, Jihao Long, Lei Wu
We consider the problem of learning functions within the and Barron spaces, which play crucial roles in understanding random feature models (RFMs), two-layer ne…
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
Hongrui Chen, Holden Lee, Jianfeng Lu
We give an improved theoretical analysis of score-based generative modeling. Under a score estimate with small error (averaged across timesteps), we provide efficient converg…