In this thesis we target development of hybrid quantum-classical machine learning algorithms with following properties: executable on currently available quantum platform in the cloud; demonstrate provable quantum advantage; applicable to real world use-cases. We explore the quantum generative modelling, the task of training an algorithm to generalise from a finite set of samples drawn from a data set, by learning the underlying probability distribution from which these samples are drawn. The model can then generate new samples from the target distribution itself. The concrete benchmarking goal is to demonstrate that fewer samples are required in comparison with the classical statistical inference.
Keywords : generative modelling, statistical inference, quantum computing, cryptanalysis
This PhD research project has been submitted for a funding request to “Sorbonne Center for Artificial Intelligence (SCAI)”. The PhD candidate selected by the project leader will therefore participate in the project selection process (including a file and an interview) to obtain funding.