[25p-71B-1~13] 10.4 Spintronics in semiconductor, topological material, superconductor, and multiferroics

3:15 PM - 3:30 PM JST(6:15 AM - 6:30 AM UTC)

[25p-71B-8] Channel Attention enhances Quantum Convolutional Neural Networks for Quantum Phase Recognition

〇Gekko Patria Budiutama^1,2, Shunsuke Daimon³, Hirofumi Nishi^1,2, Ryui Kaneko^4,5, Tomi Ohtsuki⁵, Yu-ichiro Matsushita^1,2,3 (1.Quemix, 2.Tokyo Tech, 3.QST, 4.Waseda, 5.Sophia)

Keywords:

Quantum machine learning,Quantum neural network,Quantum computing

Quantum Convolutional Neural Networks (QCNNs) have gained attention as a promising approach to quantum machine learning. In this study, we propose integrating a channel attention mechanism based on measurements of quantum bits into QCNNs. The introduction of the channel attention mechanism reduces the training cost of QCNNs and enhances their performance in quantum phase recognition.