Abstract In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.
Abstract In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.
The rapid advancements in quantum computing have opened new avenues for enhancing classical machine learning paradigms, particularly in the realm of image classification. Traditional Deep Convolutional Neural Networks (DCNNs) have achieved remarkable success, yet they face challenges related to computational intensity and the need for vast datasets [4, 19, 31]. Quanvolutional Neural Networks (QNNs) emerge as a promising hybrid quantum-classical approach that integrates quantum circuits directly into the feature extraction process of convolutional layers. This article explores the fundamental characteristics and operational advantages of QNNs, focusing on how their unique quantum-enhanced feature maps contribute to improved image classification performance. We delve into the architecture of quanvolutional layers, the mechanisms of data encoding, and the potential for quantum advantage in feature learning. By synthesizing recent research, we demonstrate the theoretical underpinnings and observed benefits of QNNs in extracting richer, more discriminative features, potentially leading to higher accuracy and efficiency, especially in the Noisy Intermediate-Scale Quantum (NISQ) era [22]. Challenges such as data encoding complexity, parameter optimization, and hardware limitations are also discussed, alongside future directions for scalable and robust QNN implementations.
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.
Abstract In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.
Image classification and image processing are critical tasks that form the basis for problems in computer vision. Recently, numerous classification techniques based on quantum machine learning have been proposed, such as quanvolutional neural network (QNN) - a hybrid quantum-classical model which has the potential to process high-resolution images and outperform current image processing techniques. In this article, we investigate the use of entangled topologies in QNN to extract features more efficiently. We also propose a training strategy for the quantum part of the QNN model. The results show that with a valid hyperparameter, our QNN achieves higher accuracy than CNN with a significantly smaller number of parameters.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs’ application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs’ utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs’ robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques.