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TELKOMNIKA Indonesian Journal of Electrical Engineering

Study on Application of Quantum BP Neural Network to Curve Fitting

Zhixia Zhang, Yanchang Liu, Jianping Xie

Published Jul 4, 2013• 1 citation
Publisher

Abstract

Based on the Quantum mechanics in a Quantum phase-shifted door and two negates controlled both general door, using them as the basic unit composed of activation function neural Network, this paper puts forward a Quantum state with input and output of the Quantum neuron Quantum state model and three layers of Quantum BP neural Network model of Network learning algorithm and performance to carry on the thorough study. Experimental results show that compared with BP neural Network, QBP appears quickly learn and convergent effect, with better fitting ability than the BP neural Network. DOI:  http://dx.doi.org/10.11591/telkomnika.v11i10.3388

DOI
10.11591/telkomnika.v11i10.3388

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Presents a quantum BP neural network model and its improved learning algorithm.Firstly a quantum neuron is constructed based on the universality of single qubit rotation gate and two-qubit controlled not gate.Secondly,the hide layer is constructed and uses these quantum neurons with the application of the gradient descent algorithm and the output layer is made with traditional neurons,in which weight and bias values are updated according to the improved algorithm of gradient descent momentum and an adaptive learning rate.It is shown that this model and algorithm are superior to the conventional BP networks in two aspects:convergence speed and accuracy by two UCI's data sets examples.
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.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
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