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 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 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.
Hybrid quantum neural networks represent a novel approach to enhancing computational performance in deep learning tasks. By integrating quantum-inspired techniques with classical neural network architectures, these models aim to leverage the computational advantages of quantum computing while retaining the scalability and interpretability of classical methods. This paper explores the design and implementation of hybrid quantum neural networks, focusing on their ability to optimize computational efficiency in deep learning tasks. We present a comprehensive review of quantum-inspired optimization techniques and their application in neural network training. Through empirical evaluations and comparative studies, we demonstrate the effectiveness of hybrid quantum neural networks in improving training speed, convergence, and generalization performance. This work contributes to the advancement of efficient deep learning methodologies and lays the groundwork for further exploration of quantum-inspired approaches in machine learning. DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.231-234
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 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.
In recent years, the field of machine learning has witnessed a paradigm shift with the emergence of hybrid quantum-classical neural networks. These networks combine the power of classical neural networks with the computational advantages offered by quantum variational circuits. This study presents a comprehensive survey of hybrid quantum-classical machine learning algorithms, focusing on their architecture, performance, and hyperparameter optimization techniques. Introducing the concept of hybrid quantum-classical machine learning and its significance in contemporary image classification tasks. It explores the theoretical foundations of quantum variational circuits and their integration with classical neural network architectures. Special attention is given to prominent algorithms such as Quantum Convolutional Neural Networks, Quanvolutional Neural Networks, and Quantum ResNet, each offering unique advantages in image classification tasks. A major aspect covered in this study is the hyperparameter optimization techniques employed in hybrid quantum-classical machine learning. The chapter discusses quantum-inspired optimization methods tailored for efficiently tuning the hyperparameters of complex neural network models. Through comparative analysis, the effectiveness of these optimization techniques is demonstrated in reducing computational overhead and improving model performance. Moreover, the study also delves into experimental results obtained from benchmarking hybrid quantum-classical models against traditional classical approaches. It highlights the accuracy variations observed across different quantum architectures and explores the impact of architectural permutations on model convergence. Insights gained from these experiments provide valuable guidance for researchers and practitioners seeking to leverage hybrid quantum-classical neural networks in real-world applications.
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.