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.
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.
Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O (( log 2 M ) 6 ) basic gates and O ( m 2 + e ) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.
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.
Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision. This model can also be used in small quantum computers, and a hybrid learning model can be designed by adding a quantum convolution layer to the CNN model or replacing it with a convolution layer. This paper also verifies whether the QCNN model is capable of efficient learning compared to CNN through training using the MNIST dataset through the TensorFlow Quantum platform.
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.
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.
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.