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.
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.
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 Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.
Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.
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.
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.
Convolutional neural networks (CNNs) are extensively employed for tasks such as image processing, employing a hierarchical feature extraction approach facilitated by convolutional layers. A recent trend explores quantum counterparts called Quanvolutional layers, utilizing random quantum circuits akin to convolutional filter layers. Our work advances this by introducing structured entanglement in quanvolutional layers using Basic and Strongly Entangled Layers. Unlike random circuit approaches, these layers have defined structures and high entanglement, requiring minimal error correction. We evaluated their performance on MNIST dataset, comparing various techniques like traditional CNNs, Quanvolutional Neural Networks (QNNs) with Random quantum circuits, basic entangled layers, strongly entangled layers. Structured entanglement shows promise in improving feature extraction for QNNs, providing insights into their understanding. Our work explores the use of structures quantum layers in constructing quantum-classical hybrid architectures and evaluates their performance. Future research may explore scalability and applicability in complex datasets and integration with other quantum computing techniques for enhanced performance and versatility.