To enhance the approximation and generalization ability of classical artificial neural networks

a quantum-inspired neural network model

whose input of each dimension is a discrete sequence

is proposed.This model concludes three layers

in which the hidden layer consists of quantum-inspired neurons

and the output layer consists of common neurons.The quantum-inspired neuron consists of the quantum rotation gates and the multi-qubits controlled-rotation gates.By using the information feedback of target qubit from output to input in multi-qubits controlled-rotation gate

the overall memory of input sequences is realized.The output of quantum-inspired neuron is obtained from the entanglements of multi-qubits in controlled-rotation gates.The learning algorithm is designed in detail according to the basic principles of quantum computation.The characteristics of input sequence can be effectively obtained by way of breadth and depth.The simulation results show that

when the input nodes and the length of the sequence satisfy a certain relations

the proposed model is obviously superior to the common artificial neural networks.