Research And Application Of Quantum Computation And Process Neural Networks

AbstractQuantum computation is a novel inter-discipline that includes quantum mechanics and information science.The quantum algorithm based on the basic principles of quantum computation has been widely concerned by scholars around the; world,and has shown the very broad application prospects.Process neural network (PNN) is a new artificial neural network (ANN) model proposed at the beginning of this century according to information processing mechanism of the biological nervous system in conjunction with the application of practical problems.The process, input of the PNN remove the instantaneous synchronization constraints for input in traditional ANN model. The process neural network is an extension model of traditional ANN in the time domain, and it is a more generalized ANN model. The research content of this thesis can be summarized as follows.Firstly, a quantum BP neural networks model and algorithm based on quantum rotation gates and quantum controlled-not gates are proposed, and then the continuity of the model is proved The experimental results of the sunspot number prediction show that the predictive power of this model is superior to common BP networks. By simulating biological neural information processing mechanism, a quantum weight neural network model is presented with both the quantum linked weight and the quantum activation value.Using gradient descent algorithm, a super-linearly convergent learning algorithm of this model is designed. The availability of the approach is illustrated by two application examples of pattern recognition and function approximation; by analyzing the physical meaning of quantum gates, a quantum gates neural network model and algorithm are introduced with quantum gate nodes.This model can significantly increase the probability of convergence because of its implicit multi-attractors.Simulation results show that the convergence speed and prediction capabilities are significantly better than that of the ordinary ANN.Secondly, the learning algorithms of PNN are developed based on Legendre orthogonal basis functions, which can effectively solve complex computing problems of spatial and temporal aggregation. For the issue of parallel processing of massive samples, a parallel process neural networks model and algorithm are constructed. This method can not only decentralized networks load, but also improve the predictive power of a single networks.Thirdly, on the basis of analyzing the existing problems in current quantum search algorithm, two improved algorithms are proposed by constructing the new quantum search engines.A quantum search algorithm is presented based on weighted targets, in which the successful probability of each marked item is consistent with the corresponding weight coefficient. Namely, the greater successful probability is gotten for the more important target. An improved quantum search algorithm with small phase rotations is proposed. When the size of phase rotations are fixed at 0.01Ï€, the success probability, at least 99.99% can be obtained.Fourthly, by analying the problems existing in current quantum evolutionary algorithm, a quantum genetic algorithm is proposed based on the spherical coordinates of quantum bit, and two new quantum gate operators are designed. In this algorithm, by regarding coordinates of the qubit as approximate solutions of the optimization problem, this algorithm can increase the solution space traversal arid the probability of convergence.By directly taking the qubit phase as a gene on chromosome,four quantum-inspired optimization algorithms are respectively presented. In these algorithms, the optimization process is performed in [-1,1]n or [0,2Ï€]n,which has nothing to do with specific issues, therefore, the proposed methods have good adaptability for a variety of optimization problems.By combining these algorithms into the process of neural network training, the computational efficiency and prediction accuracy of model can be significantly improved.Fifthly, on the basis of the Mid-and-Long term forecasting of monthly discharge time series, the simulation comparisons of eight intelligent optimization models are studied. Through applying these model to the two monthly discharge time series of hydropower station of the Manwan and the Hongjiadu, the prediction results show that the quantum-inspired optimization algorithms are superior to ordinary genetic algorithm,and quantum neural networks are superior to common BP neural networks, which verifies the introduction of quantum mechanisms can improve the forecasting performance of the traditional model and algorithm,and can increase the forecasting accuracy of monthly discharge time series. Finally, all the studies in this text were summarized, and some new topics are discussed.