29 citations · 149 across the 27 of their papers we have counts for
4 papers · 1 filter
On fundamental aspects of quantum extreme learning machines
Weijie Xiong, Giorgio Facelli, Mehrad Sahebi +4
Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a…
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling
Teerachote Pakornchote, Natthaphon Choomphon-anomakhun, Sorrjit Arrerut +4
The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symme…
Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
Apimuk Sornsaeng, Ninnat Dangniam, Thiparat Chotibut
Next Generation Reservoir Computing (NG-RC) is a modern class of model-free machine learning that enables an accurate forecasting of time series data generated by dynamical systems…
StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks
Teerachote Pakornchote, Annop Ektarawong, Thiparat Chotibut
Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic scale ab initio approaches are compu…