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Journal of Nanjing University of Posts and Telecommunications

Quantum BP Neuron and Its Application in Recognition of Noisy English Letter

Guangqiang Zhang

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

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.

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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.
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
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.
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.
A quantum BP neural networks model with learning algorithm is proposed.Firstly,based on the universality of single qubit rotation gate and two-qubits controlled NOT gate,a quantum neuron model is constructed,which is composed of input,phase rotation,aggregation,reversal rotation,and output.In this model,both the input and the output are described by qubits.The phase rotation and inversion are performed by the universal quantum gates.Secondly,the quantum BP neural networks model based on quantum neurons is constructed.On the basic of the gradient descent algorithm,a learning algorithm of the model is proposed.The model is applied to simulate the relation between well logging and water-flooded level which can achieve so that water-flooded layer recognition can be achieved with in oil well logging.Testing results indicate that the method is effective.
To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.