In this research, a Hybrid Quantum Convolutional Neural Network (HQCNN) was proposed for Symmetry-Protected Topological (SPT) Phase data Classification. The HQCNN used quantum Convolutional and pooling layers with traditional fully connected networks. The quantum Convolutional (QConv) and quantum pooling (QPool) layers were developed using quantum circuit states. The TensorFlow quantum package was used to add the outcomes of the quantum circuits to dense layers for efficient classification. A different number of quantum filters were used in the HQCNN model and tested. A different number of quantum filters were used in the HQCNN model design. The performance of the HQCNN models with the different number of quantum filters was compared using validation and testing performance. The simulation results showed that the classification accuracy of the proposed HQCNN with Four Quantum Filters (HQCNN-4QF) was superior to other proposed HQCNNs on the SPT phase data Classification. The HQCNN-4QF model achieved the classification accuracy of 99.22 on SPT phase test data.
In this research, a Hybrid Quantum Convolutional Neural Network (HQCNN) was proposed for Symmetry-Protected Topological (SPT) Phase data Classification. The HQCNN used quantum Convolutional and pooling layers with traditional fully connected networks. The quantum Convolutional (QConv) and quantum pooling (QPool) layers were developed using quantum circuit states. The TensorFlow quantum package was used to add the outcomes of the quantum circuits to dense layers for efficient classification. A different number of quantum filters were used in the HQCNN model and tested. A different number of quantum filters were used in the HQCNN model design. The performance of the HQCNN models with the different number of quantum filters was compared using validation and testing performance. The simulation results showed that the classification accuracy of the proposed HQCNN with Four Quantum Filters (HQCNN-4QF) was superior to other proposed HQCNNs on the SPT phase data Classification. The HQCNN-4QF model achieved the classification accuracy of 99.22 on SPT phase test data.
A particle generated inside a group cannot be described independently of the other group particles during the interaction, sharing, or the development of a particle's group in close geographic in the quantum mechanics. Particles' arrangement represents their quantum states at zero temperature and highlights the particle's energy and symmetry gap during the Symmetry Protected Topology SPT as the “trivial” topological instructions' existence of these particles and provides a more consistent method for differentiating the quantum data source's features such as a quantum sensor or an intricate simulation of a device through quantum entanglement. In this research utilizing the TensorFlow framework with a cluster state as a data source which mayor may not excited, the researcher demonstrates and evaluates a quantum variant of the conventional classical neural network (CNN) called the hybrid quantum-classical convolutional neural network (HQCNN) which is trained on data containing a quantum mechanical state protected by the SPT topology. The accuracy was determined in purely model's results did not exceed 89.58% accuracy within ten epochs, whereas the hybrid model's results were accurate to 100% within three epochs with a single (Twist) filter and seven epochs with a multi-filter (multi-Twist) model with the goal of eliminating quantum entanglement.
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.
Abstract With the significant enhancement of quantum computing capabilities and their expanding applications in various fields, quantum image processing combined with quantum computing has become a key area of research in quantum computing, holding important theoretical value. In the face of the challenges posed by the digital information era, image classification technology demands higher standards from machine learning algorithms in terms of accuracy, computational speed, generalization capability, and complexity. This paper studies a series of quantum data encoding methods and parametric quantum circuit architectures, and proposes the application of Hybrid Hierarchical Encoding (HHE) and Hybrid Quantum Convolutional Neural Network (HQCNN). The new encoding method HHE integrates the benefits of angle encoding and enhances the hybrid amplitude encoding design to achieve optimal classification performance without compromising the depth of quantum circuits.HQCNN demonstrates outstanding classification performance and remarkable control over model complexity by leveraging the data processing capabilities of classical convolution and the complex computational power of quantum convolution. Experimental results show that HHE and HQCNN achieve better classification accuracy on the MNIST and Fashion-MNIST datasets compared to traditional classical CNN models, particularly when the number of parameters is significantly reduced.
Quantum computing, based on fundamental quantum-mechanical principles and phenomena such as superposition, entanglement, and interference, offers access to high-dimensional quantum feature spaces and complex correlations that may be difficult to efficiently represent using classical computing, along with potential computational advantages through quantum hardware. Therefore, quantum computing has emerged as a promising paradigm for addressing limitations of classical deep learning (DL) through fundamentally different computational mechanisms. Quantum computing-based DL approaches have been applied to image classification; however, most rely on existing quantum computing libraries that impose substantial computational costs, limiting their broader applicability. In this study, hybrid quantum convolutional neural networks (HQCNNs) are proposed for structural damage classification without relying on existing quantum computing libraries. The proposed architectures integrate quanvolutional layers, composed of encoding, variational quantum circuits, and measurement, with conventional convolutional layers in a CNN architecture, enabling quantum-enhanced feature extraction while maintaining compatibility with classical computing systems. The scratch-implemented HQCNN (HQCNN(S)), its library-based counterpart (HQCNN(P)), and the classical CNN are compared in terms of test accuracy, computational cost, and training efficiency. Experimental results demonstrate that HQCNN(S) ( at ) achieves up to 176.57 faster computation than HQCNN(P) and 3.1% higher test accuracy than the classical CNN, while reducing the number of trainable parameters by 60.2%. Moreover, HQCNN(S) achieves comparable performance to the best-performing classical CNN using 75% less training data.
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.
Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.