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Analysis of learnability of a novel hybrid quantum-classical convolutional neural network in image classification

Chinese Physics B 2024年第4期33卷 275-283页

作者：程涛赵润盛王爽王睿马鸿洋 School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum *** 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 *** 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 ***,we use the MNIST dataset to test the potential of the *** results indicate that the HQCCNN has good performance in solving classification *** 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.

关键词： parameterized quantum circuits quantum machine learning hybrid quantum-classical convolutional neural network

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quantum self-attention neural networks for text classification

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Science China(Information Sciences) 2024年第4期67卷 301-313页

作者： Guangxi LI Xuanqiang ZHAO Xin WANG Institute for quantum Computing Baidu Research Centre for quantum Software and Information University of Technology Sydney QICI quantum Information and Computation Initiative Department of Computer ScienceThe University of Hong Kong Thrust of Artificial Intelligence Information HubHong Kong University of Science and Technology(Guangzhou)

An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing(NLP). Although some efforts based on syntactic analysis have opened the door to research in quantum NLP(QNLP), limitations such as heavy syntactic preprocessing and syntax-dependent network architecture make them impracticable on larger and real-world data sets. In this paper, we propose a new simple network architecture, called the quantum selfattention neural network(QSANN), which can compensate for these limitations. Specifically, we introduce the self-attention mechanism into quantum neural networks and then utilize a Gaussian projected quantum self-attention serving as a sensible quantum version of self-attention. As a result, QSANN is effective and scalable on larger data sets and has the desirable property of being implementable on near-term quantum devices. In particular, our QSANN outperforms the best existing QNLP model based on syntactic analysis as well as a simple classical self-attention neural network in numerical experiments of text classification tasks on public data sets. We further show that our method exhibits robustness to low-level quantum noises and showcases resilience to quantum neural network architectures.

关键词： quantum neural networks self-attention natural language processing text classification parameterized quantum circuits

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