3 papers
quant-ph2025
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…
cs.LG2025
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…
physics.chem-ph2025
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…