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...
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IEEE
2025-01-01
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| author | Li Hai Chen Liang Hao Yaming Yu Wenli Shi Fengquan |
| author_facet | Li Hai Chen Liang Hao Yaming Yu Wenli Shi Fengquan |
| author_sort | Li Hai |
| collection | DOAJ |
| description | 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 neural networks (CNNs), we introduce a novel quantum variant: the Quantum Pseudo-Transposed Convolutional Neural Network (QPTCNN). The QPTCNN adapts the concept of classical transposed CNNs to the quantum domain, leveraging a hybrid quantum-classical framework that combines a quantum convolutional layer with a classical fully connected layer. In the QPTCNN, the quantum convolutional layer emulates a transposed convolution operation, ensuring that the output feature map retains the same dimensions as the input image. This is accomplished using rotational angle encoding and a ring-structured quantum circuit, interconnected by two-qubit control gates such as CNOT and CRY gates, facilitating efficient quantum convolution. We evaluated the performance of the QPTCNN on the MNIST and Fashion-MNIST datasets, with two distinct versions of the model: Model A, which utilizes a CNOT-gate entanglement circuit, and Model B, which employs a CRY-gate entanglement circuit. The results demonstrate that both Model A and Model B achieve strong performance across the datasets. However, Model A outperforms Model B, achieving higher classification accuracy and lower loss compared to earlier models. These findings suggest that the QPTCNN is highly capable of learning and extracting relevant feature information from input images, making it well-suited for high-performance image classification tasks. This work represents a significant advancement in quantum-enhanced image classification. |
| format | Article |
| id | doaj-art-8271b3bbf0e648069a9f2d6c068c2eb8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-8271b3bbf0e648069a9f2d6c068c2eb82025-08-20T03:15:47ZengIEEEIEEE Access2169-35362025-01-0113371083711710.1109/ACCESS.2025.354316410891464An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural NetworksLi Hai0https://orcid.org/0000-0001-6577-2050Chen Liang1Hao Yaming2Yu Wenli3Shi Fengquan4School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaRecent 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 neural networks (CNNs), we introduce a novel quantum variant: the Quantum Pseudo-Transposed Convolutional Neural Network (QPTCNN). The QPTCNN adapts the concept of classical transposed CNNs to the quantum domain, leveraging a hybrid quantum-classical framework that combines a quantum convolutional layer with a classical fully connected layer. In the QPTCNN, the quantum convolutional layer emulates a transposed convolution operation, ensuring that the output feature map retains the same dimensions as the input image. This is accomplished using rotational angle encoding and a ring-structured quantum circuit, interconnected by two-qubit control gates such as CNOT and CRY gates, facilitating efficient quantum convolution. We evaluated the performance of the QPTCNN on the MNIST and Fashion-MNIST datasets, with two distinct versions of the model: Model A, which utilizes a CNOT-gate entanglement circuit, and Model B, which employs a CRY-gate entanglement circuit. The results demonstrate that both Model A and Model B achieve strong performance across the datasets. However, Model A outperforms Model B, achieving higher classification accuracy and lower loss compared to earlier models. These findings suggest that the QPTCNN is highly capable of learning and extracting relevant feature information from input images, making it well-suited for high-performance image classification tasks. This work represents a significant advancement in quantum-enhanced image classification.https://ieeexplore.ieee.org/document/10891464/Convolutional neural networksquantum pseudo-transposed convolutional neural networksquantum circuitstransposed convolution |
| spellingShingle | Li Hai Chen Liang Hao Yaming Yu Wenli Shi Fengquan An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks IEEE Access Convolutional neural networks quantum pseudo-transposed convolutional neural networks quantum circuits transposed convolution |
| title | An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks |
| title_full | An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks |
| title_fullStr | An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks |
| title_full_unstemmed | An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks |
| title_short | An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks |
| title_sort | improved convolutional neural networks quantum pseudo transposed convolutional neural networks |
| topic | Convolutional neural networks quantum pseudo-transposed convolutional neural networks quantum circuits transposed convolution |
| url | https://ieeexplore.ieee.org/document/10891464/ |
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