  We show that the Quantum Generative Adversarial Network (QGAN) paradigm can
be employed by an adversary to learn generating data that deceives the
monitoring of a Cyber-Physical System (CPS) and to perpetrate a covert attack.
As a test case, the ideas are elaborated considering the navigation data of a
Micro Aerial Vehicle (MAV). A concrete QGAN design is proposed to generate fake
MAV navigation data. Initially, the adversary is entirely ignorant about the
dynamics of the CPS, the strength of the approach from the point of view of the
bad guy. A design is also proposed to discriminate between genuine and fake MAV
navigation data. The designs combine classical optimization, qubit quantum
computing and photonic quantum computing. Using the PennyLane software
simulation, they are evaluated over a classical computing platform. We assess
the learning time and accuracy of the navigation data generator and
discriminator versus space complexity, i.e., the amount of quantum memory
needed to solve the problem.
