Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analysis how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analysis how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum– classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. This research proposes a hybrid computational approach aimed at enhancing efficiency through the integration of classical and quantum-inspired techniques. The core concept involves the utilization of quantum-inspired natural networks to optimize computational processes. By amalgamating classical computing with quantum-inspired algorithms, we seek to exploit the benefits of quantum-inspired features for improved computational efficiency. The abstract outlines the motivation, methodology, and expected outcomes of this hybrid model, emphasizing its potential to accelerate problem-solving and computation-intensive tasks. The report further delves into detailed implementation and performance assessments to validate the effectiveness of the proposed hybrid approach. DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.235-252
Inspired by the success of neural networks in the classical machine learning tasks, there has been tremendous effort to develop quantum neural networks (QNNs), especially for quantum data or tasks that are inherently quantum in nature. Currently, with the imminent advent of quantum computing processors to evade the computational and thermodynamic limitation of classical computations,} designing an efficient quantum neural network becomes a valuable task in quantum machine learning. In this paper, a novel quantum neural network with deep residual learning (ResQNN) is proposed. {Specifically, a multiple layer quantum perceptron with residual connection is provided. Our ResQNN is able to learn an unknown unitary and get remarkable performance. Besides, the model can be trained with an end-to-end fashion, as analogue of the backpropagation in the classical neural networks. To explore the effectiveness of our ResQNN , we perform extensive experiments on the quantum data under the setting of both clean and noisy training data. The experimental results show the robustness and superiority of our ResQNN, when compared to current remarkable work, which is from \textit{Nature communications, 2020}. Moreover, when training with higher proportion of noisy data, the superiority of our ResQNN model can be even significant, which implies the generalization ability and the remarkable tolerance for noisy data of the proposed method.
Internet of Things (IoT) is defined as millions of interconnections between wireless devices to obtain data globally. The multiple data are targeting to observe the data through a common platform, and then it becomes essential to investigate accuracy for realizing the best IoT platform. To address the growing demand for time-sensitive data analysis and real-time decision-making, accuracy in IoT data collecting has become critical. The Res-HQCNN is a hybrid quantum-classical neural network with deep residual learning. The model is qualified in an end-to-end analog method in a traditional neural network, backpropagation is used. To discover the Res-HQCNN efficiency to perform on the classical computer, there has been a lot of investigation into quantum data with or without noise. Then focus on the application of the artificial neural network to analyze the dangers to these IoT networks. For data recording purposes, to undertake in-depth analysis on the threat severity, kind, and source, a model is trained using recurrent and convolutional neural networks. The intrusion detection system (IDS) explored in this study has a success rate of 99% based on the empirical data supplied to the model. Due to irregularly distributed robust execution, larger affectability for the introduction of authority dimension, steadiness, and the extremely large crucial area, a quantum hash function work has been proposed as an amazing method for secure communication between the IoT and cloud.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.
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.