Abstract The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classification predominantly relies on traditional architectures such as variational quantum circuits. The performance of these models is closely tied to the scale of their parameters, with the substantial demand for parameters potentially leading to limitations in computational resources and a significant increase in computation time. In this study, a Lean Classical‐Quantum Hybrid Neural Network (LCQHNN) is introduced, which achieves efficient classification performance with only four layers of variational circuits, thereby substantially reducing computational costs. These experiments demonstrate that LCQHNN achieves 100%, 99.02%, and 85.55% classification accuracy on MNIST, FashionMNIST, and CIFAR‐10 datasets. Under the same parameter conditions, the convergence speed of this method is also faster than that of traditional models. Furthermore, through visualization studies, it is found that the model effectively captures key data features during training and establishes a clear association between these features and their corresponding categories. This study confirms that the employment of quantum algorithms enhances the model's ability to handle complex classification problems.
Abstract The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machine learning algorithms for image classification predominantly relies on traditional architectures such as variational quantum circuits. The performance of these models is closely tied to the scale of their parameters, with the substantial demand for parameters potentially leading to limitations in computational resources and a significant increase in computation time. In this study, a Lean Classical‐Quantum Hybrid Neural Network (LCQHNN) is introduced, which achieves efficient classification performance with only four layers of variational circuits, thereby substantially reducing computational costs. These experiments demonstrate that LCQHNN achieves 100%, 99.02%, and 85.55% classification accuracy on MNIST, FashionMNIST, and CIFAR‐10 datasets. Under the same parameter conditions, the convergence speed of this method is also faster than that of traditional models. Furthermore, through visualization studies, it is found that the model effectively captures key data features during training and establishes a clear association between these features and their corresponding categories. This study confirms that the employment of quantum algorithms enhances the model's ability to handle complex classification problems.
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.
Image classification plays a critical role across various industries, yet the rapid growth of visual data has significantly increased computational complexity. Convolutional neural networks (CNNs) have demonstrated strong capabilities in efficient feature extraction, but they still face trade‐offs between performance and computational cost when handling large‐scale data. Recent studies have explored integrating quantum mechanical principles into classical CNNs to enhance model expressivity. However, existing hybrid quantum–classical convolutional architectures remain limited in terms of feature capture and computational efficiency. In this study, we propose a novel hybrid quantum–classical convolutional neural network (QC‐CNN‐Parallel), which introduces parallel quantum and classical convolutional branches and extends the concept of multiscale convolution to hybrid networks. This design enables the capture of larger contextual information during convolution while reducing circuit depth and computational overhead. Moreover, we employ parameterized quantum circuits (PQCs) with expressibility, entanglement, and discreteness metrics to select optimal quantum circuits and further enhance model performance. We evaluate QC‐CNN‐Parallel on three grayscale image datasets: MNIST, Fashion‐MNIST, and Overhead‐MNIST. Experimental results show that our model improves average training accuracy by 4.89%, 2.77%, and 6.24% over existing hybrid quantum CNNs and classical CNNs, respectively, while exhibiting superior robustness. These results demonstrate that QC‐CNN‐Parallel achieves enhanced classification performance with high computational efficiency and practical potential.
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.
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.
The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.
Abstract The advantages of quantum neural networks in image classification have been demonstrated in recent studies, but as the number of network layers increases, the model becomes difficult to train and performs poorly. In addition, research on selecting appropriate quantum circuits for the models is still scarce. Therefore, in order to solve these problems, in this paper, based on parameterized quantum circuits, a hybrid quantum‐classical Inception neural network model for image classification is proposed. This model integrates three parallel feature extraction layers: a quantum layer, a classical layer and a hybrid quantum‐classical layer, which can fully utilize the advantages of quantum high‐dimensional computation and classical large‐scale processing. In addition, the interplay between the expressibility and entanglement of quantum circuits and the model's performance is explored, offering insights for future research. Experimental results on the CIFAR‐10 dataset demonstrate that the model achieves significantly higher classification accuracy compared to several existing approaches.