Time series classification (TSC) has attracted a lot of attention for time series data mining tasks and has been applied in various fields. With the success of deep learning (DL) in computer vision recognition, people are starting to use deep learning to tackle TSC tasks. Quantum neural networks (QNN) have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing, but research using quantum neural networks to handle TSC tasks has not received enough attention. Therefore, we proposed a learning framework based on multiple imaging and hybrid QNN (MIHQNN) for TSC tasks. We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN. We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging. Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks. We tested our method on several standard datasets and achieved significant results compared to several current TSC methods, demonstrating the effectiveness of MIHQNN. This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.
Time series classification (TSC) has attracted a lot of attention for time series data mining tasks and has been applied in various fields. With the success of deep learning (DL) in computer vision recognition, people are starting to use deep learning to tackle TSC tasks. Quantum neural networks (QNN) have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing, but research using quantum neural networks to handle TSC tasks has not received enough attention. Therefore, we proposed a learning framework based on multiple imaging and hybrid QNN (MIHQNN) for TSC tasks. We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN. We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging. Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks. We tested our method on several standard datasets and achieved significant results compared to several current TSC methods, demonstrating the effectiveness of MIHQNN. This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.
Image Classification is one of the major tasks in deep learning. Various deep learning methodologies like Quantum Neural Network are playing a vital role in classifying the images. In traditional neural networks neural layers are used to extract the features from the image to train the model where some of the details may be excluded. In quantum methodology parameterised circuits are used to identify complex boundaries to extract high quality features from the images. In the proposed method a hybrid QNN model is developed for binary image classification using quantum computing. Proposed model efficiently works by performing computation on Noise Intermediate-Scale Quantum (NISQ) devices. which represent the current forefront of quantum computing capabilities. This is achieved by integrating a small, two-qubit quantum circuit with a classical convolutional backbone using a tensor product state. The proposed model achieves a classification accuracy of 90.1% on binary image datasets, marking a significant improvement. Furthermore, we evaluate the generalization performance of both the H-QNN and baseline CNN models in image retrieval tasks. The results demonstrate the superior generalization capabilities of the H-QNN, especially in mitigating overfitting on small datasets, making it a promising solution for real-world applications.
Abstract Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitable for low-dimensional data and require dimensionality reduction and quantum encoding. Two image classification methods have recently emerged: one employs PCA for dimensionality reduction and angular encoding, and the other integrates QNN into a CNN to improve performance. Despite numerous algorithms, the differences between them remain unclear. This study explores these algorithms’ performance in multi-class image classification and proposes an optimized hybrid quantum neural network suitable for the current environment. As the number of classes increases, research on PCA-based quantum algorithms reveals the barren plateau problem of QNN, which is not suitable for multiple classes in a hybrid setting. Our proposed model combining traditional CNN with QNN addresses QNN’s multi-class training difficulties to some extent and achieves satisfactory classification results. Nevertheless, its accuracy remains below that of the top-performing CNN models. Furthermore, we investigate transfer learning in hybrid quantum neural network models and assess the performance of our models on the quantum hardware from IBM. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.
Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.
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.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
Abstract With the significant enhancement of quantum computing capabilities and their expanding applications in various fields, quantum image processing combined with quantum computing has become a key area of research in quantum computing, holding important theoretical value. In the face of the challenges posed by the digital information era, image classification technology demands higher standards from machine learning algorithms in terms of accuracy, computational speed, generalization capability, and complexity. This paper studies a series of quantum data encoding methods and parametric quantum circuit architectures, and proposes the application of Hybrid Hierarchical Encoding (HHE) and Hybrid Quantum Convolutional Neural Network (HQCNN). The new encoding method HHE integrates the benefits of angle encoding and enhances the hybrid amplitude encoding design to achieve optimal classification performance without compromising the depth of quantum circuits.HQCNN demonstrates outstanding classification performance and remarkable control over model complexity by leveraging the data processing capabilities of classical convolution and the complex computational power of quantum convolution. Experimental results show that HHE and HQCNN achieve better classification accuracy on the MNIST and Fashion-MNIST datasets compared to traditional classical CNN models, particularly when the number of parameters is significantly reduced.