Qubit neuron according to quantum circuit for XOR problem

This paper presents a novel learning model with qubit neuron according to quantum circuit for XOR problem and describes the influence to learning by reducing the number of neurons. Our approach has a 3-qubit neuron including a work qubit in the input layer, which employs gradient descent for learning. For improving the learning efficiency, furthermore, a momentum term is added to gradient descent. For the number of neurons in the output layer, the convergence rate and the average iteration for learning are examined. Especially it is exhibited that our model can learn for one neuron in the output layer. Experimental results are presented in order to show that our approach is effective in the convergence rate and the average iteration.