A learning algorithm and its applications to the quantum neural network model
一种量子神经网络模型学习算法及应用

A quantum neural network model and its learning algorithm are presented. According to the information processing mode of the biology neuron and the quantum computing theory, we first propose a quantum neuron model which includes weighting, aggregating, activating, and prompting. Secondly, the quantum neural network model based on quantum neuron is constructed in which both the input and the output are real vectors and both the linked weight and the activation value are qubits. Using gradient descent algorithm, we also propose a super-linearly convergent learning algorithm of the quantum neural network. Finally, the availability of the approach is illustrated by two application examples of pattern recognition and function approximation.