Adaptive Network-Based Quantum Fuzzy Inference System

In this paper, a quantum fuzzy inference system based on adaptive network (ANQFIS) is proposed based on ANFIS and quantum BP (QBP) neural network. Different from ANFIS, ANQFIS combines the strength of fuzzy rules with QBP in the way of quantum gate rotation, and finally takes the measurement probability of quantum states as the output. The addition of QBP makes the output accuracy of the model higher, and the calculation speed of the model is improved by virtue of the speed advantage of quantum computing. According to the gradient descent method, the parameters learning algorithm of the system is given. In the simulation experiment, low-dimensional data and high-dimensional data are used as data sets to train the model, and attack algorithms are used to generate adversarial examples for testing. The results show that ANQFIS is superior to ANFIS and QBP in output accuracy and robustness.