Variational shadow quantum neural network based on immune optimisation algorithm

Quantum neural networks are neural network models based on the principles of quantum mechanics, a combination of the advantages of classical neural networks and quantum information, using quantum bits instead of neurons in classical neural networks and quantum gates instead of activation functions in classical neural networks. Immune algorithms are a class of algorithms based on the theory of artificial immune systems, which implement functions similar to the biological immune system in terms of antigen recognition, cell differentiation, memory and self-regulation, using a population search strategy and iterative computation to eventually obtain the optimal solution to the problem with a higher probability. Quantum neural networks, one of the most important approaches in the field of quantum machine learning, can be implemented by means of variational quantum circuits. However, the classification training accuracy of variational quantum algorithms is not very high. In this paper, we propose a variational shadow quantum neural network based on the immune optimisation algorithm to classify data in order to improve the training accuracy and also the training speed. Experiments show that the immune optimisation algorithm-based variational shadow quantum neural network can not only improve the training accuracy, but also the training speed.