A learning algorithm for quantum neuron

Most proposals for quantum neural networks (QNN) have skipped over the problem of how to learn the networks. This paper describes a novel model for quantum neuron and proposes its learning algorithm. It can be shown that this algorithm works on quantum systems and the convergence result demonstrates efficient performance of the proposed quantum neuron. Numerical and graphical results show that this single quantum neuron can perform the XOR function unrealizable with a classical neuron and can eliminate the necessity of building a network of neurons to obtain nonlinear mapping.