4 citations · 6 across the 7 of their papers we have counts for
7 papers
Approximate Amplitude Encoding with the Adaptive Interpolating Quantum Transform
Gekko Budiutama, Shunsuke Daimon, Xinchi Huang +2
Amplitude encoding of real-world data on quantum computers is often the workflow bottleneck: direct amplitude encoding scales poorly with input size and can offset any speedups in…
Quantum electrometry in a silicon carbide power device
Yuichi Yamazaki, Akira Kiyoi, Naoyuki Kawabata +7
For high-bias operation devices such as silicon carbide (SiC) power devices, early detection of failure mechanisms is essential to ensure reliability. This requires a method to map…
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…
Qubit encoding for a mixture of localized functions
Taichi Kosugi, Shunsuke Daimon, Hirofumi Nishi +2
One of the crucial generic techniques for quantum computation is amplitude encoding. Although several approaches have been proposed, each of them often requires exponential classic…
Channel Attention for Quantum Convolutional Neural Networks
Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi +3
Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. Reduction in the cost of training as wel…