Abstract

Can be both open-ended or well-defined (depending on student interest).

Open-ended version: you will read and understand recent papers on near-term quantum algorithms (choice according to your interest), implement a simulation into code and hopefully suggest improvements to the algorithm.

Well-defined version: you will focus on active machine learning variational quantum algorithms to solve chemistry problems (i.e. finding ground state energies of compounds like H2O , O2 etc.).

An even more well-defined version: you will optimise existing company code of standard quantum algorithms (such as Shor’s, Grover’s, eigensolver) or a particular algorithm recently invented by the company.