To tackle the climate crisis, we need nothing short of a clean energy power system that an electrified society can rely on. Optimization problems that grid operators must solve regularly become significantly more complex as the grid employs more variable sources such as wind and solar. Hence we can no longer rely on traditional methods for the grid of tomorrow. Xiangyue Wang is a DPhil student at the University of Oxford, where he works to create solutions that provide the much-needed speedup using the quantum computers we have today. His goal is to demonstrate an experimental advantage over classical methods in optimal power flow and unit commitment problems for a power grid based on 100% clean energy. To do so, he is developing quantum machine learning algorithms that utilize a team of NISQ-era quantum computers and classical computers.

 Xiangyue is also the co-founder of NARA, a climate tech company that utilizes ML to combat climate change by generating energy retrofit recommendations and sourcing clean electricity for buildings.