Chaotic time series prediction by qubit neural network with complex-valued representation

Abstract

A qubit neural network (QNN) is a neural network that incorporates the quantum computing and representation. QNN is constructed from a set of qubit neuron model, of which internal state is a coherent superposition of qubit states. This paper evaluates the performance of QNN through a prediction of well-known Lorentz attractor, which produces chaotic time series by three dynamical systems. The experimental results show that QNN can predict time series more precisely, compared with conventional (real-valued) neural networks.