Quantum Computing Meets Deep Learning: A QCNN Model for Accurate and Efficient Image Classification

Sunil Prajapat, Manish Tomar, Pankaj Kumar (), Rajesh Kumar and Athanasios V. Vasilakos ()
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Sunil Prajapat: Department of Computer Engineering, AI Security Research Center, Gachon University, Seongnam 13120, Republic of Korea
Manish Tomar: Department of Physics and Astronomical Sciences, Central University of Himachal Pradesh, Dharamshala 176215, India
Pankaj Kumar: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India
Rajesh Kumar: Department of Physics and Astronomical Sciences, Central University of Himachal Pradesh, Dharamshala 176215, 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, 2025, vol. 13, issue 19, 1-26

Abstract: In deep learning, Convolutional Neural Networks (CNNs) serve as fundamental models, leveraging the correlational structure of data for tasks such as image classification and processing. However, CNNs face significant challenges in terms of computational complexity and accuracy. Quantum computing offers a promising avenue to overcome these limitations by introducing a quantum counterpart-Quantum Convolutional Neural Networks (QCNNs). QCNNs significantly reduce computational complexity, enhance the models ability to capture intricate patterns, and improve classification accuracy. This paper presents a fully parameterized QCNN model, specifically designed for Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model employs two-qubit interactions throughout the algorithm, leveraging parameterized quantum circuits (PQCs) with rotation and entanglement gates to efficiently encode and process image data. This design not only ensures computational efficiency but also enhances compatibility with current quantum hardware. Our experimental results demonstrate the model’s notable performance in binary classification tasks on the MNIST dataset, highlighting the potential of quantum-enhanced deep learning in image recognition. Further, we extend our framework to the Wine dataset, reformulated as a binary classification problem distinguishing Class 0 wines from the rest. The QCNN again demonstrates remarkable learning capability, achieving 97.22% test accuracy. This extension validates the versatility of the model across domains and reinforces the promising role of quantum neural networks in tackling a broad range of classification tasks.

Keywords: quantum computing; machine learning; image classification; quantum convolutional neural network; MNIST dataset; wine dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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