Multiclass image classification is considered a challenging task in computer vision that requires correctly classifying an image into one of the multiple distinct groups.In recent years, quantum machine learning has emerged as a topic of significant interest among researchers.Using quantum concepts such as superposition and entanglement, quantum machine learning algorithms provide a more efficient method of processing and classifying high-dimensional image data.This paper proposes a new image classification model using quantum-inspired convolutional neural network architecture or, shortly, QCNN.The proposed model consists of two main phases; pre-processing and classification based on the QCNN phase.Seven benchmark datasets with different characteristics are adopted to evaluate the performance of the proposed model.The experimental results revealed that the proposed QCNN outperformed its classical version.Additionally, the results demonstrated the effectiveness of the proposed model compared with the state-of-the-art models.
Multiclass image classification is considered a challenging task in computer vision that requires correctly classifying an image into one of the multiple distinct groups.In recent years, quantum machine learning has emerged as a topic of significant interest among researchers.Using quantum concepts such as superposition and entanglement, quantum machine learning algorithms provide a more efficient method of processing and classifying high-dimensional image data.This paper proposes a new image classification model using quantum-inspired convolutional neural network architecture or, shortly, QCNN.The proposed model consists of two main phases; pre-processing and classification based on the QCNN phase.Seven benchmark datasets with different characteristics are adopted to evaluate the performance of the proposed model.The experimental results revealed that the proposed QCNN outperformed its classical version.Additionally, the results demonstrated the effectiveness of the proposed model compared with the state-of-the-art models.
Technological developments in machine learning have opened up new avenues of advancement almost in every sector. The main challenge is communicating effectively with machines and enhancing their classification abilities. Convolutional Neural Network (CNN) is a deep learning technique in pattern recognition that effectively utilizes data correlation information. In contrast, CNN can only train effectively if the data or model dimension is manageable. Quantum CNN (QCNN) offers a new approach to this problem using quantum computing techniques to enhance the functionality of the classical CNN model. This research article provides an overview of the implementation of deep learning with quantum computing models for image classification. The paper also discusses the modelling approach for CNN in classical and quantum domains. In this paper, we present a model for multiclass image classification using the traditional CNN and the CNN with the quantum layer using the MNIST and CIFAR-10 datasets. The simulation results show that the proposed QCNN model has lower loss and comparatively more enhanced performance than the traditional CNN model. Also, the proposed model performs relatively better when compared with recent state-of-the-art approaches.
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.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
Medical imaging plays an important role in the early diagnosis of various diseases. In machine learning algorithms, texture, color, and shape features are primarily used. This may result in a system with inadequate model generalization capabilities. The development of an end-to-end model that can classify unprocessed medical images is now possible by recent advances in deep learning. The heavy demands of deep learning on memory and computer resources result in further advancement in the processing of complex data. As a result, quantum computing offers potential solutions that take advantage of quantum mechanics concepts like entanglement, superposition, and interference. This chapter introduces a new medical image classification model based on the quantum convolutional neural network (QCNN). Three benchmark datasets are used to evaluate the performance of QCNN; PneumoniaMNIST, VesselMNIST3D, and brain tumor MRI images. The experimental results showed that the proposed model based on the proposed QCNN is very promising.
Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.