稳健性(进化)
计算机科学
卷积神经网络
人工智能
量子
MNIST数据库
深度学习
模式识别(心理学)
算法
物理
量子力学
生物化学
基因
化学
作者
Shui-Yuan Huang,Wan-Jia An,Zhang Deshun,Nanrun Zhou
标识
DOI:10.1016/j.optcom.2023.129287
摘要
Hybrid quantum and classical classification algorithms have provided a new solution to the classification problem with machine learning methods under a hybrid computing environment. Enlightened by the potential powerful quantum computing and the benefits of convolutional neural network, a quantum analog of the convolutional kernel of the classical convolutional neural network, i.e., quantum convolutional filter, is designed to enhance the feature extraction ability. Meanwhile, quantum convolutional layers stacked by quantum convolutional filters combine variational quantum circuits with tensor network architecture and convolution operations. In addition, a hybrid quantum–classical convolutional neural network model containing quantum convolution layers and classical networks is devised. The feasibility of the proposed hybrid model are tested on the classical MNIST dataset. Finally, the adversarial robustness of the presented hybrid network is compared with that of the classical convolutional neural network and the quanvolutional one under classical adversarial examples. It is demonstrated the presented hybrid quantum–classical convolutional neural network model outperforms the original convolutional neural network and the quanvolutional neural network in some adversarial cases.
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