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International Conference on Computer Science & Education (ICCSE)

Deep web interfaces classification using QCGBP network

Liang Zhang, Yuliang Lu, Jinhong Liu

Published Aug 1, 2010• 3 citations
Publisher

Abstract

This paper proposes a quantum neural network model using conjugate gradient back-propagation algorithm or QCGBP. The QCGBP model is constructed explicitly. Meanwhile, the learning rules for parameters of the network are also presented. According to the experiment result on TEL-8 datasets, QCGBP network presents better performance than conventional BP network in accelerating the convergence rate without decreasing the precise of classification.

DOI
10.1109/iccse.2010.5593580

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This paper proposes a quantum neural network model using conjugate gradient back-propagation algorithm or QCGBP. The QCGBP model is constructed explicitly. Meanwhile, the learning rules for parameters of the network are also presented. According to the experiment result on TEL-8 datasets, QCGBP network presents better performance than conventional BP network in accelerating the convergence rate without decreasing the precise of classification.
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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.
This paper introduces a new type of deep learning method named Deep Quantum Network (DQN) for classification. DQN inherits the capability of modeling the structure of a feature space by fuzzy sets. At first, we propose the architecture of DQN, which consists of quantum neuron and sigmoid neuron and can guide the embedding of samples divisible in new Euclidean space. The parameter of DQN is initialized through greedy layer-wise unsupervised learning. Then, the parameter space of the deep architecture and quantum representation are refined by supervised learning based on the global gradient-descent procedure. An exponential loss function is introduced in this paper to guide the supervised learning procedure. Experiments conducted on standard datasets show that DQN outperforms other feed forward neural networks and neuro-fuzzy classifiers.
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