Image Classification Using Hybrid Classical-Quantum Neutral Networks

Ling, Ya-Qi; Zhang, Jun-Hu; Zhang, Li-Hua; Li, Yan-Ran; Huang, Hui-Lei

doi:10.1007/s10773-024-05669-w

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2024

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Cited by 2 publications

References 49 publications

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Integrating Quantum Principles with Classical Transfer Learning Approaches for Pneumonia Detection from Chest Radiographs

Baral,

Roy

2024

2024 4th International Conference on Sustainable Expert Systems (ICSES)

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Integrating Quantum Principles with Classical Transfer Learning Approaches for Pneumonia Detection from Chest Radiographs

Baral,

Roy

2024

2024 4th International Conference on Sustainable Expert Systems (ICSES)

View full text Add to dashboard Cite

Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification

Ranga,

Prajapat,

Akhtar

et al. 2024

Mathematics

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Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.

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Image Classification Using Hybrid Classical-Quantum Neutral Networks

Cited by 2 publications

References 49 publications

Integrating Quantum Principles with Classical Transfer Learning Approaches for Pneumonia Detection from Chest Radiographs

Integrating Quantum Principles with Classical Transfer Learning Approaches for Pneumonia Detection from Chest Radiographs

Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification

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