Quantum Neural Networks with Application in Adjusting PID Parameters

A quantum neural networks model with learning algorithm is presented. First, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and prompting. Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors, the linked weight and the activation value are Q-bits. On the basic of gradient descent algorithm, a learning algorithm is proposed. It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting optimum solution. Finally, the availability of the approach is illustrated with application of by adjusting PID controller parameters.