> Nonlinear science is fundamental, as nonlinear phenomenon occurs frequently, e.,g, chaos, but usually too hard to solve due to its nonlinear nature. We provide a quantum algorithm that solves nonlinear algebraic equation of arbitrary polynomial types in polylogarithmic time, meanwhile the problem is not practically tractable from a classical devices for very high degree polynomials. As some applications, our work suggests a new tool to deal with nonlinear differential equation, and potential usage in algebraic geometry, such as computing intersection of algebraic varieties.
[8 March 2024] My work ``Alternative Method for Estimating Betti Numbers'' has been posted to arXiv:2403.04686
[24 Dec 2024] My work with advisor Prof. Tzu-Chieh Wei ``Improved Quantum Algorithms for Eigenvalues Finding and Gradient Descent'' has been posted to arXiv:2312.14786
> In this work we leverage the block encodings method to dramatically improve two previously known quantum algorithms. What surprised us is that even with elementary operations within the block encoding framework, highly efficient algorithm could be constructed, eliminating major components from previous methods.
[23 October 2023] I was awarded a fellowship from Center for Distributed Quantum Processing at Stony Brook University !
[5 October 2023] My work with advisor Prof. Tzu-Chieh Wei ``Quantum Algorithm for Estimating Largest Eigenvalues'' has been published in Physics Letter A
[19 September 2023] My work with Prof. Xianfeng Gu and Prof. Tzu-Chieh Wei ``Quantum Algorithm for Estimating Betti Numbers Using Cohomology Approach'' has been posted to arXiv:2309.10800v1
> This is probably one of my most proud and fascinating work ! We use de Rham cohomology and Hodge theory to tackle a very difficult task in topological data analysis, which is estimating Betti numbers of a given triangulated manifold. While the problem has been established as an #P-hard in general case, where connectivity is not known priorly, in the tailored setting, our method is efficient and can be exponentially faster than previously proposed methods
[29 August 2023] My work ``Quantum Algorithm for Computing Distances Between Subspaces'' has been posted to arXiv:2308.15432v1
[04 August 2023] My work with Prof. Tzu-Chieh Wei and Prof. Xianfeng Gu ``Constant-time Quantum Algorithm for Homology Detection in Closed Curves'' has been published in SciPost Phys. 15, 049
> This is one of my favorite work so far! Unfolding underlying properties of blackbox oracle is one of the leading themes for demonstrating quantum advantage, and only a modest number of examples exist. In this work, we show that even with some oracle usages, an intrinsic topological property of a closed curve on a triangulated manifold could be revealed, thus suggesting a fruitful domain, e.g, computational topology, might be a 'golden mineral' to invest quantum effort
> Other work from my undergraduate research journey ! This project took part when I was a summer fellowship student at Los Alamos National Laboratory. We specifically show that the barren plateaus phenomena still can present and possess detrimental issues in training quantum machine learning model, which arise mainly from the loading of dataset. Our work suggested that the choice of data embedding scheme is a crucial step in training quantum learning model
> It was a great pleasure to work under Prof. Phillip Allen! In this work, I developed a Matlab code that simulate pulse propagation in 1-dimensional atom chain in multiple settings, including anharmonic effect and mass disorder. The numerical result failed to backup theoretical prediction
> This work marked the beginning of my research journey when I was a third-year undergraduate student ! We introduced a general framework that provides a unified viewpoint for all quantum classifier models, which emphasizes the role of data embedding in quantum supervised learning. In particular, I had a chance to run some programs on real quantum hardware provided by IBM, which showed a surprisingly stellar performance in the presence of noise