An important and usual sort of search problems is to find all marked states from an unsorted database with a large number of states. Grover＇s original quantum search algorithm is for finding single marked state with ...An important and usual sort of search problems is to find all marked states from an unsorted database with a large number of states. Grover＇s original quantum search algorithm is for finding single marked state with uncertainty, and it has been generalized to the case of multiple marked states, as well as been modified to find single marked state with certainty. However, the query complexity for finding all multiple marked states has not been addressed. We use a generalized Long＇s algorithm with high precision to solve such a problem. We calculate the approximate query complexity, which increases with the number of marked states and with the precision that we demand. In the end we introduce an algorithm for the problem on a ＂duality computer＂ and show its advantage over other algorithms.展开更多
Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. T...Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.展开更多
基金4 Acknowledgements The author would like to thank G.L. Long for very helpful discussion, and thank J.Q. Yi for his generous help in plotting the function figures.
文摘An important and usual sort of search problems is to find all marked states from an unsorted database with a large number of states. Grover＇s original quantum search algorithm is for finding single marked state with uncertainty, and it has been generalized to the case of multiple marked states, as well as been modified to find single marked state with certainty. However, the query complexity for finding all multiple marked states has not been addressed. We use a generalized Long＇s algorithm with high precision to solve such a problem. We calculate the approximate query complexity, which increases with the number of marked states and with the precision that we demand. In the end we introduce an algorithm for the problem on a ＂duality computer＂ and show its advantage over other algorithms.
文摘Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.
