What we do:
The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. We explore generative Quantum Learning that cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also develop specific use cases in Finance, Cryptanalysis, Optimal Compiling for such models and compare the capabilities of quantum versus classical models for such tasks.