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

Physica Scripta

Hybrid quantum neural network based on weight remapping and its applications

Yumin Dong, Shuang Wu

Published Dec 5, 2024• 2 citations
Publisher

Abstract

Abstract In order to explore the possibility of cross-fertilization between quantum computing and neural networks, and to analyse the effects of multiple weight remapping functions on the model performance, this article proposes two hybrid models based on weight remapping: the hybrid quantum neural network (HQNN-WR) and the hybrid quantum convolutional neural network (HQCNN-WR). The HQNN-WR model uses a two-layer fully connected network to process the input features, performs feature transformation by applying multiple weight remapping functions, and subsequently passes the processed features to the quantum neural network for computation, and finally outputs the classification results. The experimental results show that the model significantly improves the classification accuracy on Iris, Wine and Breast datasets and the convergence speed is faster. The HQCNN-WR model integrates convolutional layers, pooling layers, and fully connected layers, and prevents over-fitting through a dropout layer, and exhibits excellent performance in binary classification tasks on MNIST and KMNIST datasets. The model effectively mitigates the over-fitting problem on small sample datasets and enhances the robustness and generalization ability of the model while improving the digit recognition accuracy. By comparing different models, this article also demonstrates their significant effects on the performance of hybrid quantum neural networks, providing a new theoretical basis and experimental support for the optimization and application of quantum machine learning methods.

DOI
10.1088/1402-4896/ad9ae1

Related papers

Research on image classification using quantum convolutional neural networks based on multiple approaches

Yumin Dong, Xiaofeng Ni, Tonglei Sun +2 more

Abstract With the significant enhancement of quantum computing capabilities and their expanding applications in various fields, quantum image processing combined with quantum computing has become a key area of research in quantum computing, holding important theoretical value. In the face of the challenges posed by the digital information era, image classification technology demands higher standards from machine learning algorithms in terms of accuracy, computational speed, generalization capability, and complexity. This paper studies a series of quantum data encoding methods and parametric quantum circuit architectures, and proposes the application of Hybrid Hierarchical Encoding (HHE) and Hybrid Quantum Convolutional Neural Network (HQCNN). The new encoding method HHE integrates the benefits of angle encoding and enhances the hybrid amplitude encoding design to achieve optimal classification performance without compromising the depth of quantum circuits.HQCNN demonstrates outstanding classification performance and remarkable control over model complexity by leveraging the data processing capabilities of classical convolution and the complex computational power of quantum convolution. Experimental results show that HHE and HQCNN achieve better classification accuracy on the MNIST and Fashion-MNIST datasets compared to traditional classical CNN models, particularly when the number of parameters is significantly reduced.

Physica Scripta•Mar 18, 2025•2 citations

Quantum Computing Based Neural Network Model for Digital Data Recognition

Haewon Byeon, Somarouthu V G V A Prasad, Sudipta Halder +3 more

A digital recognition paradigm integrating a hybrid quantum-traditional neural network is developed for numerical recognition, whereby quantum computing and conventional neural networks may collaborate. This model integrates parameterized quantum circuits within a convolutional framework to attain a combination of embedding and direct output. The input from the convolutional fully connected layer functions as the control parameter for the quantum layer and is subjected to a sequence of transformations for feature processing. This structure's potential is evidenced by digit recognition on the MNIST dataset for digital applications. The approach employs transfer learning, integrating convolutional and deep image feature extraction using hybrid networks, thereby mitigating the overfitting problem associated with a singular convolutional network on limited sample datasets. The findings demonstrate the model's efficacy, utility, and superior generalization capability. The recognition rates of the direct output hybrid network and the embedded hybrid network on the MNIST dataset increased by 0.0398 and 0.0211, respectively, in comparison to a convolutional network. Following transfer learning and feature fusion, the recognition rates of the hybrid model on small sample datasets exceed those of a singular network, achieving 83%, so validating the model's learning and generalization proficiency for digital applications.

OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON)•Apr 9, 2025•0 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

Hybrid Quantum Neural Networks with Group Averaging for Image Classification

Yujing Fan, Huanfeng Teng, Xiang Chen +2 more

The growing real-world deployment of computer vision technologies imposes increasingly stringent requirements on the generalization and few-shot adaptation performance of image classification models. While quantum machine learning (QML) has shown considerable promise for image classification tasks, existing hybrid quantum neural networks (HQNNs) consistently exhibit limited generalization ability and weak few-shot learning performance. We investigate the integration of standard W-group averaging into two existing HQNN architectures, HQNN-Parallel and HQNN-Quanv. Each complete input image is transformed by the four elements of the W-group and processed through a shared-parameter feature extractor. The resulting quantum feature vectors or feature maps are averaged before the final classical classification layer. This construction introduces no additional trainable parameters, but requires four transformed-input evaluations per image. The resulting models are denoted as W-HQNN-Parallel and W-HQNN-Quanv, respectively. Systematic experiments on three classic image classification datasets show that the W-HQNN-Parallel model achieves an accuracy of approximately 91.56% on the full FashionMNIST dataset and 73.96% on the full CIFAR-10 dataset respectively, outperforming the original model by around 0.50 and 2.88 percentage points, while also yielding lower test loss than HQNN-Parallel on both datasets. In single-run reduced-data experiments, W-HQNN-Quanv achieved accuracies of 68.83% on MNIST and 64.19% on FashionMNIST, exceeding the corresponding baselines by 9.60 and 6.16 percentage points, respectively. These results provide empirical evidence of the effects of W-group averaging in the two investigated HQNN architectures under the evaluated experimental settings.

Entropy•Oct 22, 2026•0 citations

Quantum-Enhanced Convolutional Neural Networks for Image Classification

Tariq Qayyum, Asadullah Tariq, Muhammad Waqas Haseeb +3 more

In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.

IEEE Future Networks World Forum (FNWF)•Nov 13, 2023•6 citations
Abstract In order to explore the possibility of cross-fertilization between quantum computing and neural networks, and to analyse the effects of multiple weight remapping functions on the model performance, this article proposes two hybrid models based on weight remapping: the hybrid quantum neural network (HQNN-WR) and the hybrid quantum convolutional neural network (HQCNN-WR). The HQNN-WR model uses a two-layer fully connected network to process the input features, performs feature transformation by applying multiple weight remapping functions, and subsequently passes the processed features to the quantum neural network for computation, and finally outputs the classification results. The experimental results show that the model significantly improves the classification accuracy on Iris, Wine and Breast datasets and the convergence speed is faster. The HQCNN-WR model integrates convolutional layers, pooling layers, and fully connected layers, and prevents over-fitting through a dropout layer, and exhibits excellent performance in binary classification tasks on MNIST and KMNIST datasets. The model effectively mitigates the over-fitting problem on small sample datasets and enhances the robustness and generalization ability of the model while improving the digit recognition accuracy. By comparing different models, this article also demonstrates their significant effects on the performance of hybrid quantum neural networks, providing a new theoretical basis and experimental support for the optimization and application of quantum machine learning methods.
Abstract With the significant enhancement of quantum computing capabilities and their expanding applications in various fields, quantum image processing combined with quantum computing has become a key area of research in quantum computing, holding important theoretical value. In the face of the challenges posed by the digital information era, image classification technology demands higher standards from machine learning algorithms in terms of accuracy, computational speed, generalization capability, and complexity. This paper studies a series of quantum data encoding methods and parametric quantum circuit architectures, and proposes the application of Hybrid Hierarchical Encoding (HHE) and Hybrid Quantum Convolutional Neural Network (HQCNN). The new encoding method HHE integrates the benefits of angle encoding and enhances the hybrid amplitude encoding design to achieve optimal classification performance without compromising the depth of quantum circuits.HQCNN demonstrates outstanding classification performance and remarkable control over model complexity by leveraging the data processing capabilities of classical convolution and the complex computational power of quantum convolution. Experimental results show that HHE and HQCNN achieve better classification accuracy on the MNIST and Fashion-MNIST datasets compared to traditional classical CNN models, particularly when the number of parameters is significantly reduced.
A digital recognition paradigm integrating a hybrid quantum-traditional neural network is developed for numerical recognition, whereby quantum computing and conventional neural networks may collaborate. This model integrates parameterized quantum circuits within a convolutional framework to attain a combination of embedding and direct output. The input from the convolutional fully connected layer functions as the control parameter for the quantum layer and is subjected to a sequence of transformations for feature processing. This structure's potential is evidenced by digit recognition on the MNIST dataset for digital applications. The approach employs transfer learning, integrating convolutional and deep image feature extraction using hybrid networks, thereby mitigating the overfitting problem associated with a singular convolutional network on limited sample datasets. The findings demonstrate the model's efficacy, utility, and superior generalization capability. The recognition rates of the direct output hybrid network and the embedded hybrid network on the MNIST dataset increased by 0.0398 and 0.0211, respectively, in comparison to a convolutional network. Following transfer learning and feature fusion, the recognition rates of the hybrid model on small sample datasets exceed those of a singular network, achieving 83%, so validating the model's learning and generalization proficiency for digital applications.
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 growing real-world deployment of computer vision technologies imposes increasingly stringent requirements on the generalization and few-shot adaptation performance of image classification models. While quantum machine learning (QML) has shown considerable promise for image classification tasks, existing hybrid quantum neural networks (HQNNs) consistently exhibit limited generalization ability and weak few-shot learning performance. We investigate the integration of standard W-group averaging into two existing HQNN architectures, HQNN-Parallel and HQNN-Quanv. Each complete input image is transformed by the four elements of the W-group and processed through a shared-parameter feature extractor. The resulting quantum feature vectors or feature maps are averaged before the final classical classification layer. This construction introduces no additional trainable parameters, but requires four transformed-input evaluations per image. The resulting models are denoted as W-HQNN-Parallel and W-HQNN-Quanv, respectively. Systematic experiments on three classic image classification datasets show that the W-HQNN-Parallel model achieves an accuracy of approximately 91.56% on the full FashionMNIST dataset and 73.96% on the full CIFAR-10 dataset respectively, outperforming the original model by around 0.50 and 2.88 percentage points, while also yielding lower test loss than HQNN-Parallel on both datasets. In single-run reduced-data experiments, W-HQNN-Quanv achieved accuracies of 68.83% on MNIST and 64.19% on FashionMNIST, exceeding the corresponding baselines by 9.60 and 6.16 percentage points, respectively. These results provide empirical evidence of the effects of W-group averaging in the two investigated HQNN architectures under the evaluated experimental settings.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.