Quantum Intelligent Algorithm And Its Application

Quantum information science is a rising cross discipline. Due to its unique features in the information field, it may break the limitation of classic information system. Particularly in recent years, quantum intelligence algorithms based on the parallel quantum computation can effectively simplify computation complexity of some classic algorithms.First of all, we improved the quantum genetic algorithm. Simulation results show that the improved quantum genetic algorithm performs obviously superior to the classic quantum genetic algorithm.Secondly, a novel Quantum Particle Swarm Optimization (QPSO) algorithm is proposed, which combine Particle Swarm Optimization (PSO) with real coded quantum genetic algorithm. Simulation results demonstrate the superiority of the proposed QPSO.Thirdly, this paper combined QGA with immune clone select algortithm and chaotic and proposed a new algorithm-Chaotic Immune Quantum Genetic Algorithm (CIQGA). Simulation results show the superiority of the proposed CIQGA.Finally, all the proposed algorithms in this paper were applied to initialization of cluster analysis of Speech Recognition System based on Hidden Markov Models (HMM). The experiment results show that 2.5% error reduction is achieved on our proposed algorithms based speaker-independent digit speech recognition system compared to K-means clustering algorithm based speech recognition system.