4 citations · 6 across the 7 of their papers we have counts for
3 papers · 1 filter
Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning
Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi +3
Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits req…
General Transform: A Unified Framework for Adaptive Transform to Enhance Representations
Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi +1
Discrete transforms, such as the discrete Fourier transform, are widely used in machine learning to improve model performance by extracting meaningful features. However, with numer…
Towards Improved Quantum Machine Learning for Molecular Force Fields
Yannick Couzinié, Shunsuke Daimon, Hirofumi Nishi +3
This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based…