A quantum neural networks model and its learning algorithm are presented. Firstly, a quantum controlled-rotating
gate is proposed by analyzing the meaning of the controlled-NOT gate. Then a quantum neuron model is constructed from
the physical meaning of the controlled rotating gate. The model includes two kinds of design parameters, rotation angle and
its control range. Secondly, a quantum neural networks model based on quantum neuron is proposed. By using gradient
descent algorithm, a learning algorithm of this model is designed in detail. With applications of pattern recognition and time
series prediction, the simulation results show that the proposed algorithm is superior to the common BP neural networks in
both convergence capability and robustness.