  Generalization is an important feature of neural network, and there have been
many studies on it. Recently, with the development of quantum compu-ting, it
brings new opportunities. In this paper, we studied a class of quantum neural
network constructed by quantum gate. In this model, we mapped the feature data
to a quantum state in Hilbert space firstly, and then implement unitary
evolution on it, in the end, we can get the classification result by im-plement
measurement on the quantum state. Since all the operations in quan-tum neural
networks are unitary, the parameters constitute a hypersphere of Hilbert space.
Compared with traditional neural network, the parameter space is flatter.
Therefore, it is not easy to fall into local optimum, which means the quantum
neural networks have better generalization. In order to validate our proposal,
we evaluated our model on three public datasets, the results demonstrated that
our model has better generalization than the classical neu-ral network with the
same structure.
