Quantum Adversarial Learning for Hyperspectral Remote Sensing

Adversarial learning is of paramount importance in numerous recent computing areas, while quantum entanglement has also revealed its role in efficient computing. This motivated us to bring quantum computing into adversarial learning. Though there are some preliminary theoretical studies of quantum adversarial learning (QAL), there has no demonstration of advanced real-world applications. For the first time, we design and implement a QAL framework, and demonstrate the feasibility of QAL-based hyperspectral image processing for remote sensing. This research line aims to lay the foundation for the QAL in hyperspectral remote sensing, including to solve the limited qubit resources, to mitigate the quantum collapse effect, to design a quantum discriminator with comparable ability, as well as to ensure the quantum expressibility of the generator, to name a few.