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Journal of Jiangsu Radio & Television University

Research on Quantum Neuron Characteristics Based on Ack Propagation(BP)

Shengmei Zhao

Published Jan 1, 2006• 0 citations
OpenAlex

Abstract

Quantum Neural Networks(QNN) Integrates the characteristics of artificial neural networks by quantum neuron computing,and becomes a promising field in information processing.A quantum neuron model by the conventional back propagation with learning rule,is established based on the phase-shift gate and thecontrolled-Not gate acted as the basic computational component.Finally its convergence performance curve of the quantum neuron is given by the numerical simulation.

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Quantum Neural Networks(QNN) Integrates the characteristics of artificial neural networks by quantum neuron computing,and becomes a promising field in information processing.A quantum neuron model by the conventional back propagation with learning rule,is established based on the phase-shift gate and thecontrolled-Not gate acted as the basic computational component.Finally its convergence performance curve of the quantum neuron is given by the numerical simulation.
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.
Abstract Quantum computation is well known for its particular computational performance. In this paper, a novel quantum neuron model is constructed based on the universal quantum gates unit (i.e. phase-shift gate and controlled-NOT gate), which acts as the basic computational component, with this type of neuron a feed-forward network structure is also built. The simulation results show that the quantum neuron network is superior to classical BP and RBF network for two financial data analysis examples.
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 Networks (QNN) is naturally the next step in the evolution of neuro-computing system. QNN may give us unprecedented possibilities in creating new system for information processing. The fundamentals of QNN are introduced, a model for quantum neuron is described. A quantum learning algorithm is proposed and its convergence property is discussed. The computational power of the quantum neuron is explored. Numerical and graphical results show that this single quantum neuron can perform XOR function unrealizable with a classical neuron and has the same computational power as the two-layer NN, it can be used to realize nonlinear mapping.
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
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.