Degraded parameter estimation using quantum neural network

In this paper, an approach based on the quantum neural network is investigated to guide the process of selecting an optimal estimation of Gaussian degraded parameter. In fact, we first formulate the nonlinear problem by maximum likelihood estimation. Then we modify and apply the quantum neural network algorithm, which combines the advantages of both quantum computing and neural computing, to solve the optimal estimation problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional techniques. The simulation results indicate the soundness of the new method.