4 citations · 4 across the 5 of their papers we have counts for
5 papers
Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition
Kota Ichiki, Xinchi Huang, Gekko Budiutama +7
Quantum computing offers a promising approach to accelerating partial differential equation (PDE) solvers for large-scale, real-world problems. However, reconstructing the classica…
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…
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…
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…