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arXiv:1911.02998 · Science China Physics Mechanics and Astronomy

Hybrid quantum-classical convolutional neural networks

Junhua Liu, Kwan Hui Lim, Kristin Lee Wood, Wei Huang, Chu Guo, He-Liang Huang

Published Aug 4, 2021• 291 citations
arXivHTMLPDF
DOI
10.1007/s11433-021-1734-3

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We design a new hybrid quantum–classical convolutional neural network (HQCCNN) model based on parameter quantum circuits. In this model, we use parameterized quantum circuits (PQCs) to redesign the convolutional layer in classical convolutional neural networks, forming a new quantum convolutional layer to achieve unitary transformation of quantum states, enabling the model to more accurately extract hidden information from images. At the same time, we combine the classical fully connected layer with PQCs to form a new hybrid quantum–classical fully connected layer to further improve the accuracy of classification. Finally, we use the MNIST dataset to test the potential of the HQCCNN. The results indicate that the HQCCNN has good performance in solving classification problems. In binary classification tasks, the classification accuracy of numbers 5 and 7 is as high as 99.71%. In multivariate classification, the accuracy rate also reaches 98.51%. Finally, we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
Significant advances have been made in quantum computing, enabling researchers to create hybrid quantum- classical learning models for various tasks, including medical image classification. The classical approach to deep learning often faces challenges with limited data availability, high computational costs, and poor generalization. The hybrid Quantum- Classical Convolutional Neural Networks (HQCNNs) provide a new direction for overcoming the challenges faced by classical approaches. The hybrid approach enables the use of quantum circuits for feature encoding in higher-dimensional space, improving discrimination between patterns and reducing overfitting. In this paper, a hybrid quantum-classical convolutional neural network is developed for medical image classification. The hybrid approach is expected to capture non-linear relationships within medical image data while maintaining a lightweight network structure. The hybrid approach is found to have significant potential for efficient operation with limited data availability and noisy data. The classical approach is often outperformed by the hybrid approach. The focus of this paper is on architectural design and analysis of the developed hybrid quantum-classical convolutional neural network. The advantages of the developed hybrid quantum-classical convolutional neural network are de- lineated, including current challenges with quantum hardware.