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Recognition of Noisy English Letter by Quantum Back Propagation Network

Shengmei Zhao, Jianhua Huang, Baoyu Zheng

Published Jan 1, 2006• 2 citations
Publisher

Abstract

It is suggested that a type of artificial neural network can be built using the principles of quantum information processing. A new technique for the recognition of noisy English letter using quantum neural network is proposed in this paper. A qubit neuron model based on quantum mechanics, constructed the quantum back propagation learning rule network (QBP) is given, and a high reliability has been achieved. The numerical experiments of the identification of noisy English letters based on the quantum network has been performed, it is shown that QBP can identify the noised English letters efficaciously; the recognition rate may reach the 100% when the variance of the noise is less than 0.35, and has more improvement performance in anti-noise than that of conventional back propagation neural network, the error recognition rate decreases from 20.46% to 3 % when the noise variance is set to 0.40

DOI
10.1109/icosp.2006.345748

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It is suggested that a type of artificial neural network can be built using the principles of quantum information processing. A new technique for the recognition of noisy English letter using quantum neural network is proposed in this paper. A qubit neuron model based on quantum mechanics, constructed the quantum back propagation learning rule network (QBP) is given, and a high reliability has been achieved. The numerical experiments of the identification of noisy English letters based on the quantum network has been performed, it is shown that QBP can identify the noised English letters efficaciously; the recognition rate may reach the 100% when the variance of the noise is less than 0.35, and has more improvement performance in anti-noise than that of conventional back propagation neural network, the error recognition rate decreases from 20.46% to 3 % when the noise variance is set to 0.40
Quantum Neural Networks(QNN),which integrates the characteristics of artificial neural networks with quantum computing,will become a promising field of information processing.In this paper,a quantum neuron model based on the phase-shift gate and controlled-not gate is constructed with the conventional back propagation learning rule.Its convergence performance is discussed by numerical simulations.Based on this model,a three-layers quantum networks is proposed.The numerical experiments of the identification of noisy English letters show that the proposed quantum networks can identify the noisy English letters efficaciously when the noise variance is less than 0.35,and has more improvement performance in anti-noise than that of conventional Back-Propagation Neural Network.
In recent years, some researchers have been exploring quantum computers in view of neural networks to realize a distributed and strongly connectionist system that achieves parallel and fast information processing. We (1998) have proposed and investigated a qubit neuron model based on quantum mechanics, and constructed the quantum backpropagation learning rule (QBP). In this paper, we show an improved QBP neural network model and discuss its performance on solving the 4 bit parity check problem and the function identification problem. Then, we conclude that our model is more effective than the conventional one in information processing efficiency.
Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks (QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Back-propagation (BP) algorithm and particle swarm optimization (PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.
Quantum Neural Network (QNN), a burgeoning new field which integrates quantum computation with classical neural network, can improve the inadequacies of artificial neural network. A model of quantum neuron and Quantum Back Propagation (BP) Neural Network based on quantum neuron are investigated. A novel approach toward speech enhancement which adopts Quantum BP Neural Network is proposed. The numerical simulation results show that the proposed speech enhancement scheme resulted in better performance than traditional spectral subtraction, Wiener filtering method and RBF neural network.
Concerns the use of quantum computer methods to develop a distributed and strongly connectionist system that achieves parallel and fast information processing. We have proposed a qubit-like neuron model based on quantum mechanics and constructed the quantum backpropagation learning rule (QBP). In this paper, we show our improved QBP neural network model and discuss its performance on solving the 4 bit parity check problem, the function and the gray-scale pattern identification problem. Then, we find our model is more excellent than the conventional one in information processing efficiency.