Referring to the relational concepts and principles of quantum computing, an improved real-coded quantum
evolutionary algorithm is proposed. The core of this algorithm is that, a complementary mutation operator, which is designed
based on the specific configuration of real-coded chromosome and the gradient information of objective function, is used
to update chromosomes and can treat the balance between exploration and exploitation. And a technique of dynamic
reducing the search space is adopted to improve the convergence rate of algorithm, which is implemented on the basis
of the evolutionary process of algorithm. Simulation results on benchmark numerical optimization show that the algorithm
has the characteristics of more powerful optimizing ability, higher searching precision and better stability. Finally, with the
parameter estimation of nonlinear system, simulation experiments are performed and the results show that the algorithm can
improve the precision of estimation parameters efficiently.