In this paper, an innovative Quantum image recognition algorithm, Quantum Auto Gradient Descent (QAGD), is proposed. The algorithm combines the advantages of quantum convolutional neural networks (QCNN), autoencoders (AE) and low-lot gradient descent (MGD). The introduction of an automatic encoder into the quantum convolutional neural network can effectively extract image features and reduce dimensionality, reduce data dimensions while retaining key information, and improve the model's ability to capture image features. At the same time, combined with the small-batch gradient descent optimization strategy, the model parameters are iteratively updated through small-batch data, which speeds up the model convergence speed and improves the training efficiency. In the experimental part, we compare QAGD with traditional image recognition algorithms, and the results show that QAGD algorithm has significant advantages in improving image recognition performance and reducing false positive rate, with higher recognition accuracy and faster training speed. This fully verifies the feasibility and effectiveness of QAGD architecture in the application of image recognition algorithms based on quantum convolutional neural networks.
In this paper, an innovative Quantum image recognition algorithm, Quantum Auto Gradient Descent (QAGD), is proposed. The algorithm combines the advantages of quantum convolutional neural networks (QCNN), autoencoders (AE) and low-lot gradient descent (MGD). The introduction of an automatic encoder into the quantum convolutional neural network can effectively extract image features and reduce dimensionality, reduce data dimensions while retaining key information, and improve the model's ability to capture image features. At the same time, combined with the small-batch gradient descent optimization strategy, the model parameters are iteratively updated through small-batch data, which speeds up the model convergence speed and improves the training efficiency. In the experimental part, we compare QAGD with traditional image recognition algorithms, and the results show that QAGD algorithm has significant advantages in improving image recognition performance and reducing false positive rate, with higher recognition accuracy and faster training speed. This fully verifies the feasibility and effectiveness of QAGD architecture in the application of image recognition algorithms based on quantum convolutional neural networks.
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.
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.
Quantum machine learning is emerging as a promising frontier at the intersection of quantum computing and Artificial Intelligence, offering potential gains in speed, efficiency, and robustness. In this paper, a hybrid Quantum Convolutional Neural Network (QCNN) architecture is presented for visual pattern recognition tasks, leveraging quantum superposition and entanglement for enhanced feature extraction. Unlike previous studies that focus on small-scale or simplified datasets, this study systematically evaluates QCNNs across multiple benchmark datasets and provides a direct comparison with classical CNNs, highlighting practical quantum advantages such as parameter efficiency and noise-resilience. The proposed approach encodes classical image data from benchmark datasets, including70K28×28grayscale images from MNIST and FashionMNIST, and60Kdownsampled grayscale CIFAR-10 images, into quantum states using amplitude encoding, followed by hierarchical quantum convolution and pooling layers implemented via parametrised quantum circuits (PQCs). The model culminates in a classical dense layer for final classification. Experimental results demonstrate that the QCNN achieves competitive performance on low-dimensional grayscale datasets with 93.2 % accuracy on MNIST and 88.5% on Fashion MNIST, though it underperforms on more complex datasets like CIFAR-10 with 67.8 % accuracy due to current quantum hardware limitations. While not yet surpassing classical CNNs in raw accuracy, the proposed QCNN exhibits notable parameter efficiency, noise resilience, and theoretical potential for energy-efficient computation, highlighting its promise for future applications as quantum technologies mature.
CNN (Convolutional neural network) is an excellent machine learning model, especially in image recognition, where they have excellent results. However, it is difficult to learn effectively if the given dimensionality of the data or model is too large. QCNN QCNN (Quantum Convolutional Neural Network) offer new solutions to the problems faced by CNN using a quantum computing environment and provide directions for improving the performance of existing learning models. However, most of the current QCNN models are only simulation programs running on a classical computer and cannot actually run on a quantum computer. In this paper, we propose a QCNN model with a quantum encoding circuit that can encode classical data into quantum data, allowing the QCNN to run on quantum computers. Firstly, we introduce the method of quantum state amplitude encoding to represent the high-dimensional classical data into the form of quantum states, constructing a bridge between traditional data and quantum state transformation, while significantly reducing the dimensionality of the processed data. Secondly, we construct the QCNN model and analyze how to implement the image recognition function by QCNN network and the optimization method of the model. Finally, we validate the learning effect of the model using MNIST handwritten digits and compare and discuss the learning effect of QCNN with classical models.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.
A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.