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

Deepak Ranga, Sunil Prajapat, Zahid Akhtar, Pankaj Kumar () and Athanasios V. Vasilakos ()
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Deepak Ranga: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Sunil Prajapat: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Zahid Akhtar: Department of Network and Computer Security, State University of New York Polytechnic Institute, C135, Kunsela Hall, Utica, NY 13502, USA
Pankaj Kumar: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Athanasios V. Vasilakos: Department of Networks and Communications, College of Computer Science and Information Technology, IAU, P.O. Box 1982, Dammam 31441, Saudi Arabia

Mathematics, 2024, vol. 12, issue 23, 1-22

Abstract: 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.

Keywords: quantum computing; machine learning; Hybrid Quantum Neural Networks; MNIST dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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