Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Advanced Quantum Technologies

Hybrid Quantum‐Classical Inception Neural Network for Image Classification

Lu Wang, Yuxiang Liu, Jinpeng Ji, Fanxu Meng, Zaichen Zhang, Xutao Yu

Published Mar 20, 2025• 3 citations
Publisher

Abstract

Abstract The advantages of quantum neural networks in image classification have been demonstrated in recent studies, but as the number of network layers increases, the model becomes difficult to train and performs poorly. In addition, research on selecting appropriate quantum circuits for the models is still scarce. Therefore, in order to solve these problems, in this paper, based on parameterized quantum circuits, a hybrid quantum‐classical Inception neural network model for image classification is proposed. This model integrates three parallel feature extraction layers: a quantum layer, a classical layer and a hybrid quantum‐classical layer, which can fully utilize the advantages of quantum high‐dimensional computation and classical large‐scale processing. In addition, the interplay between the expressibility and entanglement of quantum circuits and the model's performance is explored, offering insights for future research. Experimental results on the CIFAR‐10 dataset demonstrate that the model achieves significantly higher classification accuracy compared to several existing approaches.

DOI
10.1002/qute.202400700

Related papers

A Parallel Hybrid Quantum‐Classical Convolutional Design Using Parameterized Quantum Circuits for Image Classification

Haoxuan Liu, Xiaoping Lou

Image classification plays a critical role across various industries, yet the rapid growth of visual data has significantly increased computational complexity. Convolutional neural networks (CNNs) have demonstrated strong capabilities in efficient feature extraction, but they still face trade‐offs between performance and computational cost when handling large‐scale data. Recent studies have explored integrating quantum mechanical principles into classical CNNs to enhance model expressivity. However, existing hybrid quantum–classical convolutional architectures remain limited in terms of feature capture and computational efficiency. In this study, we propose a novel hybrid quantum–classical convolutional neural network (QC‐CNN‐Parallel), which introduces parallel quantum and classical convolutional branches and extends the concept of multiscale convolution to hybrid networks. This design enables the capture of larger contextual information during convolution while reducing circuit depth and computational overhead. Moreover, we employ parameterized quantum circuits (PQCs) with expressibility, entanglement, and discreteness metrics to select optimal quantum circuits and further enhance model performance. We evaluate QC‐CNN‐Parallel on three grayscale image datasets: MNIST, Fashion‐MNIST, and Overhead‐MNIST. Experimental results show that our model improves average training accuracy by 4.89%, 2.77%, and 6.24% over existing hybrid quantum CNNs and classical CNNs, respectively, while exhibiting superior robustness. These results demonstrate that QC‐CNN‐Parallel achieves enhanced classification performance with high computational efficiency and practical potential.

Quantum Engineering•Jan 1, 2026•0 citations

Hybrid Neural Network Based on Parameterized Quantum Circuits for Image Classification

Jun Zhang, Xiling Xue, Yue Ruan

Image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous challenges when dealing with high-dimensional and complex data, particularly in terms of feature extraction and data processing capabilities, limiting their performance in certain applications. Quantum computing offers promising new approaches to processing high-dimensional complex data, most notably through Parameterized Quantum Circuits (PQC) as quantum hybrid neural networks for image classification. In this paper, we propose a hybrid neural network featuring a specific PQC structure designed to generate entanglement in accordance with sample images. This PQC structure utilizes the adjacency matrix of the corresponding graph of the sample image, embedding adjacency relationships among nodes into the entanglement module, thereby generating entanglement aligned with the features of specific images. Numerical results validate the effectiveness of our PQC model across various image classification tasks, enhancing the efficiency of feature learning and representation. Our model demonstrates significant advantages in classification accuracy and learning efficiency on classical datasets. Our research not only advances the theoretical understanding of hybrid quantum networks but also paves the way for practical implementations in real-world scenarios.

Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC)•Nov 4, 2024•3 citations

Analysis of learnability of a novel hybrid quantum–classical convolutional neural network in image classification

Tao 涛 Cheng 程, Run-Sheng Zhao, Shuang 爽 Wang 王 +2 more

We design a new hybrid quantum–classical convolutional neural network (HQCCNN) model based on parameter quantum circuits. In this model, we use parameterized quantum circuits (PQCs) to redesign the convolutional layer in classical convolutional neural networks, forming a new quantum convolutional layer to achieve unitary transformation of quantum states, enabling the model to more accurately extract hidden information from images. At the same time, we combine the classical fully connected layer with PQCs to form a new hybrid quantum–classical fully connected layer to further improve the accuracy of classification. Finally, we use the MNIST dataset to test the potential of the HQCCNN. The results indicate that the HQCCNN has good performance in solving classification problems. In binary classification tasks, the classification accuracy of numbers 5 and 7 is as high as 99.71%. In multivariate classification, the accuracy rate also reaches 98.51%. Finally, we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.

Chinese Physics B•Dec 28, 2023•8 citations

Hybrid quantum inception-inspired convolutional neural network for image classification

Wanqing Wu, Yuxiang Zhang

Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.

The Journal of Supercomputing•Dec 4, 2025•3 citations

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Wei Li, Peng-Cheng Chu, Guang-Zhe Liu +3 more

Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.

Quantum Engineering•Jul 14, 2022•51 citations

A Hybrid Quantum–Classical Convolutional Neural Network for Enhanced Image Classification

D. Deepika, K Vanishree, Munishamaiah Krishna

The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.

International Journal of Computer Applications•Dec 18, 2025•0 citations
Abstract The advantages of quantum neural networks in image classification have been demonstrated in recent studies, but as the number of network layers increases, the model becomes difficult to train and performs poorly. In addition, research on selecting appropriate quantum circuits for the models is still scarce. Therefore, in order to solve these problems, in this paper, based on parameterized quantum circuits, a hybrid quantum‐classical Inception neural network model for image classification is proposed. This model integrates three parallel feature extraction layers: a quantum layer, a classical layer and a hybrid quantum‐classical layer, which can fully utilize the advantages of quantum high‐dimensional computation and classical large‐scale processing. In addition, the interplay between the expressibility and entanglement of quantum circuits and the model's performance is explored, offering insights for future research. Experimental results on the CIFAR‐10 dataset demonstrate that the model achieves significantly higher classification accuracy compared to several existing approaches.
Image classification plays a critical role across various industries, yet the rapid growth of visual data has significantly increased computational complexity. Convolutional neural networks (CNNs) have demonstrated strong capabilities in efficient feature extraction, but they still face trade‐offs between performance and computational cost when handling large‐scale data. Recent studies have explored integrating quantum mechanical principles into classical CNNs to enhance model expressivity. However, existing hybrid quantum–classical convolutional architectures remain limited in terms of feature capture and computational efficiency. In this study, we propose a novel hybrid quantum–classical convolutional neural network (QC‐CNN‐Parallel), which introduces parallel quantum and classical convolutional branches and extends the concept of multiscale convolution to hybrid networks. This design enables the capture of larger contextual information during convolution while reducing circuit depth and computational overhead. Moreover, we employ parameterized quantum circuits (PQCs) with expressibility, entanglement, and discreteness metrics to select optimal quantum circuits and further enhance model performance. We evaluate QC‐CNN‐Parallel on three grayscale image datasets: MNIST, Fashion‐MNIST, and Overhead‐MNIST. Experimental results show that our model improves average training accuracy by 4.89%, 2.77%, and 6.24% over existing hybrid quantum CNNs and classical CNNs, respectively, while exhibiting superior robustness. These results demonstrate that QC‐CNN‐Parallel achieves enhanced classification performance with high computational efficiency and practical potential.
Image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous challenges when dealing with high-dimensional and complex data, particularly in terms of feature extraction and data processing capabilities, limiting their performance in certain applications. Quantum computing offers promising new approaches to processing high-dimensional complex data, most notably through Parameterized Quantum Circuits (PQC) as quantum hybrid neural networks for image classification. In this paper, we propose a hybrid neural network featuring a specific PQC structure designed to generate entanglement in accordance with sample images. This PQC structure utilizes the adjacency matrix of the corresponding graph of the sample image, embedding adjacency relationships among nodes into the entanglement module, thereby generating entanglement aligned with the features of specific images. Numerical results validate the effectiveness of our PQC model across various image classification tasks, enhancing the efficiency of feature learning and representation. Our model demonstrates significant advantages in classification accuracy and learning efficiency on classical datasets. Our research not only advances the theoretical understanding of hybrid quantum networks but also paves the way for practical implementations in real-world scenarios.
We design a new hybrid quantum–classical convolutional neural network (HQCCNN) model based on parameter quantum circuits. In this model, we use parameterized quantum circuits (PQCs) to redesign the convolutional layer in classical convolutional neural networks, forming a new quantum convolutional layer to achieve unitary transformation of quantum states, enabling the model to more accurately extract hidden information from images. At the same time, we combine the classical fully connected layer with PQCs to form a new hybrid quantum–classical fully connected layer to further improve the accuracy of classification. Finally, we use the MNIST dataset to test the potential of the HQCCNN. The results indicate that the HQCCNN has good performance in solving classification problems. In binary classification tasks, the classification accuracy of numbers 5 and 7 is as high as 99.71%. In multivariate classification, the accuracy rate also reaches 98.51%. Finally, we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.
Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.
The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.