Study On Theory And Application Of Hybrid Quantum Optimization Algorithm

Quantum computation method, developed rapidly in 1990s, is known as one of the developing trends of computing science in the future, which is based on quantum computer and proved theoretically to have prominent computing speed, storage capability with exponential level, and more stabile and effective characteristics. The studies of quantum computation make full use of unique properties of quantum Coherence state, such as the superposition of quantum state and the entanglement of qubit, and explore probabilities of a new method to compute, code and transmit the information, which is a new approach to grope for breaking through the limit of CMOS chip.Quantum optimization algorithm is a new methods created by borrowing fundamental conception and principles from quantum theory to resolve certain problems. Quantum optimization algorithms, based on quantum computation theoey, can improve computing efficiency and prevent from dropping into a local optimum in a certain extent. In this dissertation, the proposed hybrid quantum optimization algorithms combine quantum computation with neural network, evolutionary algorithm and ant colony optimization algorithm to improve the performance of the algorithms, extend the application area of the algorithms and perfect the system of the algorithms.This dissertation makes some researches on the problems concerned in quantum optimization algorithms, the main tasks are stated as follow.The principle and improved measures of quantum evolutionary algorithm are researched. Firstly, this dissertation summarize the general situation survey of evolutionary algorithm and analyse the defects existed in evolutionary algorithm; and then combined it with quantum optimization algorithms and propose a quantum evolutionary algorithm. Meanwhile, several improved measures are also proposed.A model of BP neural network, based on improved quantum evolutionary (IQEA) algorithms, is researched, and then a new neural network (NN) training algorithm, IQEA-BP algorithm, is proposed. Firstly, the traditional QEA is improved; Secondly, the improved QEA is adopted to search the optimal combinations of weights in the solution space to conquer the defect that BP neural network is easy to fall into local optimal, then regards the finded quite optimal weight as the first value, and uses BP algorithm to obtain the accurate optimal solutions quickly, which can increase the training and prediction precision of the network.This dissertation applied IQEA-BP algorithm to predict the silicon content in hot metal of blast furnace, The test results show that IQEA-BP can obtain better forecasting results, compared with BP and QEA-BP model.Aiming at the shortcoming of optimization problems in continuous space based on ant colony optimization which is easy to fall into local optimums and has a slow convergence rate, a novel ant colony optimization algorithm based on quantum evolutionary is presented. In this algorithm, each ant position is represented by a group of quantum bits; a new quantum rotation gates are designed to update the position of the ant so as to enable the ant to move. Finally, some quantum bits are mutated by quantum non-gate so as to increase the variety of ant positions. It not only proves the convergence of the proposed algorithms through theoretical analysis, but also shows that QACO possess the more quickly speed from the angle of the time complexity. simulation experiments demonstrate that QACO can double searching space, gain better population diversity and accelerate the convergence speed and global optimal search ability, compared with the classical ant colony algorithm. Then this dissertation discusses the application based on the QACO. It mainly discussed QACO applied in the cold-rolled strip flatness recognition. On the basis of analysising the mathematical model of strip flatness signal, the flatness recognition can be attributed to a problem that the function search for the optimum. Fourthmore, this dissertation discusses some problems about QACO applied in the flatness recognition. Simulink tests demonstrate that the flatness recognition method based on QACO possesses greater accuracy, compared with the method in literature.It has some certain engineering merit.Focusing on the fuzziness problem of fault classification borders, and on the diagnostic uncertainty of overlapping data, a fault diagnosis method for furnace state based on quantum neural network(QNN) was presented. This dissertation combines quantum theory with neural network and forms into a new model namely quantum neural network.Its hidden layer nerve cell adopts multi-level transition function.By use of the learning algorithms of quantum interval and weights, it can build grade inside structure of neural network according to the sample information.Quantum neural network can give an approximate class or category probability for uncertain input. In order to improve the recognition accuracy of abnormal blast furnace fourthmore, an independent component analyze method is proposed to extract their state features focused on the nonlinear and big yawp in the process of blast furnace.namely, by use of the ICA, original data is analyzed , disposaled, and separate the state signals of fault blast furnace and extract their state eigenvector. and then generate some new low dimension and few correlations among production parameters, which can reflect the essence of the system mostly. Finally, these state eigenvectors are regarded as input vector of QNN, the experimental results demonstrate that the ICA-QNN algorithms can recognize the fault pattern of furnace state effectively and accurately.meanwhile,it also provides a new method with fault diagnosis for blast furnace state.