9 citations · 13 across the 4 of their papers we have counts for
4 papers
Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
Zhiyan Ding, Taehee Ko, Jiahao Yao +2
Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learn…
Implementation of the Density-functional Theory on Quantum Computers with Linear Scaling with respect to the Number of Atoms
Taehee Ko, Xiantao Li, Chunhao Wang
Density-functional theory (DFT) has revolutionized computer simulations in chemistry and material science. A faithful implementation of the theory requires self-consistent calculat…
Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity
Xingyi Guan, Joseph Heindel, Taehee Ko +2
We train an equivariant machine learning model to predict energies and forces for a real-world study of hydrogen combustion under conditions of finite temperature and pressure. Thi…
Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset
Taehee Ko, Joseph Heindel, Xingyi Guan +3
We use local diffusion maps to assess the quality of two types of collective variables (CVs) for a recently published hydrogen combustion benchmark dataset~\cite{guan2022benchmark}…