Speaker
Description
Generalized probabilistic theories (GPTs) are a general framework to describe physical theories like quantum mechanics and classical mechanics. At their core, GPTs model prepare-and-measure scenarios by describing preparations and measurements within ordered vector spaces and then predicting measurement outcome probabilities. This framework has been very successful in describing and understanding correlations predicted by quantum theory. More recently, the attention has also been extended to the description of Markovian time evolution within GPTs by using positivity-preserving dynamical maps. This extension is important from a fundamental physics perspective because, for example, hypothetical non-quantum dynamics induced by gravity would be detectable via precise tests of the time evolution. Furthermore, dynamical GPTs can also be used in order to better understand quantum dynamics and may enable, for example, quantum computational advantage. In this talk, I will discuss the structure of dynamical GPTs, how they can be used to perform precision tests of quantum physics and help to understand dynamical quantum effects that may serve as precursors to applications in quantum technologies.