Incorporating Kalman Filter in the Optimization of Quantum Neural Network Parameters

Kalman filter has been used for the estimation of instantaneous states of linear dynamic systems.It is a good tool for inferring of missing information from noisy measurement. The quantum neuralnetwork is another approach to the merging of fuzzy logic with the neural network and that by theinvestment of quantum mechanics theory in building the structure of neural network. The gradientdescent algorithm has been used widely in training the neural network, but the problem of localminima is one of the disadvantages of this algorithm. This paper presents an algorithm to train thequantum neural network by using the extended kalman filter.