Protecting security of quantum neural network with sampling checks

With the development of quantum computing, application neural networks will be more and extensive, its security also face challenges. Although communication has high security, may have many internal external insecure factors in process information transmission, such as noise impact during preparation input states, privacy disclosure attacks on network structure, which cause major incidents. Because possible insecurity networks, this paper proposes a sampling method to detect state at each stage, so judge whether there are risks thus ensure their security. The provides safe basis for further research stability reliability networks.