An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
Recent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks. Building on the operational principles of classical transposed convolutional n...
Saved in:
| Main Authors: | Li Hai, Chen Liang, Hao Yaming, Yu Wenli, Shi Fengquan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10891464/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
by: Mohammed Yousif, et al.
Published: (2024-01-01) -
Process tomography of structured optical gates with convolutional neural networks
by: Tareq Jaouni, et al.
Published: (2024-01-01) -
Quantum classical hybrid convolutional neural networks for breast cancer diagnosis
by: Qiuyu Xiang, et al.
Published: (2024-10-01) -
Design and implementation of quantum hippo inspired convolutional neural networks using parametric quantum circuits for an efficient lung cancer classification
by: S. Radhika, et al.
Published: (2025-06-01) -
Application of bilateral fusion model based on CNN in hyperspectral image classification
by: Hongmin GAO, et al.
Published: (2020-11-01)