Quantum computers are the latest dream of humans in solving nondeterministic polynomial problems. These problems include machine learning and object classification. However, commercial quantum computers are still not available. In the meantime, scientists developed hybrid models by combining Convolutional Neural Networks (CNN) and Quantum Neural Networks (QNN) to apply quantum computing to artificial intelligence (AI) applications. The hybrid model accelerates the processing of an AI machine by combining the advantage of CNN in feature extraction with the parallel computing capability of QNN. In this study, we optimized the existing hybrid models with a fewer number of qubits to improve the performance of QNN on digit recognition. The study also presents the implementation of the model using Qiskit, describes the training process, and analyzes the impact of the number of qubits and quantum gates on the classification results. Experiments show that Hybrid Quantum-Classical Neural Networks (H-QNN) have the potential to outperform pure CNN. Finally, the study suggests future improvements, including the optimization of quantum algorithms and extending experiments to more complex data sets.
Quantum computers are the latest dream of humans in solving nondeterministic polynomial problems. These problems include machine learning and object classification. However, commercial quantum computers are still not available. In the meantime, scientists developed hybrid models by combining Convolutional Neural Networks (CNN) and Quantum Neural Networks (QNN) to apply quantum computing to artificial intelligence (AI) applications. The hybrid model accelerates the processing of an AI machine by combining the advantage of CNN in feature extraction with the parallel computing capability of QNN. In this study, we optimized the existing hybrid models with a fewer number of qubits to improve the performance of QNN on digit recognition. The study also presents the implementation of the model using Qiskit, describes the training process, and analyzes the impact of the number of qubits and quantum gates on the classification results. Experiments show that Hybrid Quantum-Classical Neural Networks (H-QNN) have the potential to outperform pure CNN. Finally, the study suggests future improvements, including the optimization of quantum algorithms and extending experiments to more complex data sets.
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 is the process in which a computer can perform analysis on an image and identify the class in which that particular image falls under. In conventional classification models (Convolutional Neural Networks or CNN), data collection and processing takes a large amount of time in days or even weeks. CNN models also have less scalability and require a lot of resources. To overcome this problem, quantum computing can be used not just for enhancing the scalability but it also provides exponential speedups because they have the ability to perform massively parallel computations. The problem with the CNNs is that they need large numbers of parameters and run into an overfitting problem with small datasets. This work is an implementation of Quanvolutional Neural Networks (QNN), which is a quantum-hybrid model, to recognise English characters using EMNIST letters dataset for training. The results of this model are then compared to a CNN model in terms of accuracy and loss. The results show that the use of a quantum layer can significantly increase the accuracy as well as decrease the training time of a classification model.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.
Quantum Machine Learning signifies a possible collaboration of quantum computing and artificial intelligence, presenting potential benefits in addressing intricate computational challenges. Hybrid quantum-classical neural networks (QNNs) have developed as a formidable instrument for classification tasks, utilizing quantum feature encoding and classical optimization. This research employs a hybrid Quantum Neural Network framework utilizing Google Cirq, Classical data is encoded into quantum states via Z, ZZ, Pauli, and hybrid feature maps to use the distinctive attributes of quantum processing in representing intricate interactions among characteristics. The hybrid model combines quantum circuits for feature extraction with classical neural networks for training and assessment. The performance metrics—accuracy, precision, recall, and F1-score—demonstrate that the hybrid feature map method regularly surpasses individual feature maps, especially in multi-class and binary classification problems. However, when benchmarked against classical deep learning models such as Convolutional Neural Networks (CNNs), the CNN achieved superior results in classification accuracy and efficiency. This underscores the current limitations of quantum models while highlighting their potential for further advancements.
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.
Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks (QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Back-propagation (BP) algorithm and particle swarm optimization (PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.