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Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

Chinese Physics B 2023年第12期32卷 221-230页

作者：谢建设董玉民 College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China

Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various *** the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC *** neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough ***,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC *** investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid *** explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series *** quantum circuits were also selected and designed to study the impact of quantum circuits on TSC *** tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of *** research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.

关键词： quantum neural networks time series classification time-series images feature fusion

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Continuous Variable quantum MNIST Classifiers—Classical-quantum Hybrid quantum neural networks

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Journal of quantum Information Science 2022年第2期12卷 37-51页

作者： Sophie Choe Marek Perkowski Electrical and Computer Engineering Portland State University Portland OR USA

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

关键词： quantum Computing quantum Machine Learning quantum neural networks Continuous Variable quantum Computing Photonic quantum Computing Classical quantum Hybrid Model quantum MNIST Classification

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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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Learning algorithm and application of quantum BP neural networks based on universal quantum gates

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Journal of Systems Engineering and Electronics 2008年第1期19卷 167-174页

作者： Li Panchi Li Shiyong Dept. of Control Science and Engineering Harbin Inst. of Technology Harbin 150001 P. R. China Dept. of Computer Science and Engineering Daqing Petroleum Inst. Daqing 163318 P. R. China

A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.

关键词： quantum computing universal quantum gate quantum neuron quantum neural networks

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