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.
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.
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.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
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.
This research explores quantum machine learning for image classification, comparing classical and quantum models. A classical convolutional neural network (CNN), based on the LeNet architecture, achieved 99.12% accuracy on MNIST, outperforming a quantum CNN, which attained 93.30%. Quantum encoders applied to Fashion-MNIST reached 85.60%, while quantum support vector machine (SVM), implemented for binary classification, obtained 71.34%. The study integrates hybrid quantum-classical models, leveraging quantum circuits for feature extraction. A quantum autoencoder-based classifier was introduced, demonstrating efficient data compression and classification without additional qubits. Feature encoding using RX gates mapped classical data into quantum states, and the impact of parameterized quantum circuit structures on classification accuracy was analyzed. Results indicate that specific ansatz structures yield superior accuracy, highlighting the significance of quantum circuit design. While classical models currently outperform quantum counterparts, quantum approaches reduce trainable parameters and computational complexity. Limitations in current quantum hardware, including noise and scalability, restrict performance. However, advancements in error mitigation, quantum gate efficiency, and scalable quantum architecture could bridge the gap with classical methods. These quantum techniques hold potential in aerospace applications, such as satellite image classification, anomaly detection in remote sensing, and real-time processing of high-dimensional image data. This study underscores the growing role of quantum circuits in machine learning, particularly in feature extraction and classification, suggesting that future quantum hardware could unlock new capabilities in end-to-end learning beyond hybrid approaches.
This research explores the integration of quantum computing with classical machine learning to enhance data classification tasks using Quantum Neural Networks (QNN) and Parameterized Quantum Circuits (PQC). The hybrid approach leverages the advantages of both quantum and classical systems to improve the efficiency and accuracy of data processing. In this model, data is encoded into qubits using amplitude encoding, representing input vectors as amplitudes of quantum states. The QNN is initialized by placing the qubits in superposition using Hadamard gates, followed by data encoding with parameterized rotational gates that map classical data to quantum states using rotation angles. PQC plays a central role by applying layers of parameterized quantum operations to process data in the quantum space. These parameters are optimized during the training process, where a quadratic loss function minimizes the error between the predicted quantum states and the true class labels using gradient descent. Experiments conducted on the MNIST dataset show that the hybrid the hybrid quantum-classical neural network (QCNN) with PQC achieves a classification accuracy of over 95%, highlighting its potential in machine learning applications. The results demonstrate that integrating quantum computing with classical machine learning enhances performance in complex data analysis tasks due to the exponential growth of quantum state space and the parallelism of quantum systems, making hybrid models promising for computer vision and classification tasks.