We examine the entanglement dynamics between two strongly driven atoms off-resonantly coupled with a singlemode cavity via the two-photon process with the help of negativity in two different types of initial states. T...We examine the entanglement dynamics between two strongly driven atoms off-resonantly coupled with a singlemode cavity via the two-photon process with the help of negativity in two different types of initial states. The results show that entanglement sudden death may occur under both the above conditions and the sudden death effect can be monitored by modulating the atom-cavity detunings. Furthermore, we also find an atomic decoherence-free subspace so that the initial entanglement between two atoms remains invariable in application.展开更多
This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mut...This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No 60667001)the Education Foundation of Yanbian University of China
文摘We examine the entanglement dynamics between two strongly driven atoms off-resonantly coupled with a singlemode cavity via the two-photon process with the help of negativity in two different types of initial states. The results show that entanglement sudden death may occur under both the above conditions and the sudden death effect can be monitored by modulating the atom-cavity detunings. Furthermore, we also find an atomic decoherence-free subspace so that the initial entanglement between two atoms remains invariable in application.
基金Supported by the Climbing PrOgram-National Key Project for Fundamental Research in China, Grant NSC92097
文摘This paper analyzes the optimization problem of mutation probability in genetic algorithms by applying the definition of i-bit improved sub-space. Then fuzzy reasoning technique is adopted to determine the optimal mutation probability in different conditions. The superior convergence property of the new method is evaluated by applying it to two simulation examples.
