Channel Attention for Quantum Convolutional Neural Networks

• URL: http://arxiv.org/abs/2311.02871v1
• Date: Mon, 6 Nov 2023 04:54:05 GMT
• Title: Channel Attention for Quantum Convolutional Neural Networks
• Authors: Gekko Budiutama, Shunsuke Daimon, Hirofumi Nishi, Ryui Kaneko, Tomi Ohtsuki, Yu-ichiro Matsushita
• Abstract summary: Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. We propose a channel attention mechanism for QCNNs and show the effectiveness of this approach for quantum phase classification problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 well as improvement in performance is required for practical implementation of these models. In this study, we propose a channel attention mechanism for QCNNs and show the effectiveness of this approach for quantum phase classification problems. Our attention mechanism creates multiple channels of output state based on measurement of quantum bits. This simple approach improves the performance of QCNNs and outperforms a conventional approach using feedforward neural networks as the additional post-processing.

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