Hybrid Quantum-Classical Convolutional Neural Networks for Image Classification in Multiple Color Spaces

TL;DR

This paper introduces a hybrid quantum-classical CNN that improves image classification accuracy across multiple color spaces, demonstrating advantages over classical CNNs on various datasets and highlighting the potential of quantum-enhanced models in computer vision.

Contribution

The paper presents a novel HQCNN architecture that integrates parameterized quantum circuits with classical CNNs, extending its application to non-RGB color spaces for the first time.

Findings

HQCNN outperforms classical CNN in all tested color spaces.

Achieved 94.3% accuracy in Lab color space on MNIST.

Quantum-enhanced models show promise for diverse image classification tasks.

Abstract

The growing complexity and scale of image processing tasks challenge classical convolutional neural networks (CNNs) with high computational costs. Hybrid quantum-classical convolutional neural networks (HQCNNs) show potential to improve performance by accelerating processing speed, enhancing classification accuracy, and reducing model parameters, though studies have primarily focused on the RGB color space. However, the effectiveness of HQCNNs in non-RGB color spaces, such as Lab, YCrCb, and HSV, remains largely unexplored. We propose an HQCNN to evaluate image classification across diverse color spaces. The HQCNN integrates parameterized quantum circuits (PQCs) with a classical CNN, leveraging quantum entanglement and trainable gates to enhance expressiveness across varied color representations. We assess performance on MNIST, CIFAR-10, EuroSAT, and SAT-4 datasets. Experimental results demonstrate that the HQCNN outperforms the classical CNN across all tested color spaces for the ten-class MNIST task, achieving a best accuracy of $94.3\%$ in Lab compared to $92.8\%$ in RGB for the CNN, with superior performance on other datasets in various color spaces. These findings highlight the potential of non-RGB color spaces and optimized PQC designs to improve classification performance. We provide new insights for advancing hybrid quantum-classical computer vision through optimized PQC architectures and diverse color space applications.