  A classical computer works with ones and zeros, whereas a quantum computer
uses ones, zeros, and superpositions of ones and zeros, which enables quantum
computers to perform a vast number of calculations simultaneously compared to
classical computers. In a cloud-supported cyber-physical system environment,
running a machine learning application in quantum computers is often difficult,
due to the existing limitations of the current quantum devices. However, with
the combination of quantum-classical neural networks (NN), complex and
high-dimensional features can be extracted by the classical NN to a reduced but
more informative feature space to be processed by the existing quantum
computers. In this study, we develop a hybrid quantum-classical NN to detect an
amplitude shift cyber-attack on an in-vehicle control area network (CAN)
dataset. We show that using the hybrid quantum classical NN, it is possible to
achieve an attack detection accuracy of 94%, which is higher than a Long
short-term memory (LSTM) NN (87%) or quantum NN alone (62%)
