| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-07-22 |
| タイトル |
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|
タイトル |
Channel Attention for Quantum Convolutional Neural Networks |
|
言語 |
ja |
| 言語 |
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|
言語 |
jpn |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
Budiutama Gekko
Daimon Shunsuke
Nishi Hirofumi
Kaneko Ryui
Ohtsuki Tomi
Matsushita Yu-ichiro
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| 抄録 |
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|
内容記述タイプ |
Abstract |
|
内容記述 |
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 perfor- mance are 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 feed-forward neural networks as the additional postprocessing. |
| 書誌情報 |
PHYSICAL REVIEW A
巻 110,
p. 012447,
発行日 2024-07
|
| 出版者 |
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|
出版者 |
APS |
| ISSN |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2469-9926 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
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関連識別子 |
10.1103/PhysRevA.110.012447 |
