6 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…
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…
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…
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…