The framework of Hamiltonian simulation opens up a new range of computational approaches for quantum computing. These approaches can be developed across all relevant fields of quantum computing applications, including, among others, partial differential equations (electromagnetism, fluid mechanics, etc.), quantum machine learning, finance, and various methods for solving optimization problems (both heuristic and exact).

The goal of this thesis is to identify a framework where these approaches—based on Hamiltonian simulation or block-encoding techniques—are feasible and can be written in an automated way.

This work could extend to the prototyping of a code generator, which would be tested on practical cases in collaboration with European partners (including a few months of internship within their teams).