Entangled topologies for quanvolutional neural networks in quantum image processing
Authors: 
Vu Tuan Hai, Pham The BaoAuthors Info & Claims
Pages 357 - 362
Abstract
Image classification and image processing are critical tasks that form the basis for problems in computer vision. Recently, numerous classification techniques based on quantum machine learning have been proposed, such as quanvolutional neural network (QNN) - a hybrid quantum-classical model which has the potential to process high-resolution images and outperform current image processing techniques. In this article, we investigate the use of entangled topologies in QNN to extract features more efficiently. We also propose a training strategy for the quantum part of the QNN model. The results show that with a valid hyperparameter, our QNN achieves higher accuracy than CNN with a significantly smaller number of parameters.
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Published In
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
Copyright © 2023 ACM.
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Published: 07 December 2023
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- University of Information Technology
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SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
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Overall Acceptance Rate 147 of 318 submissions, 46%
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