Zoom (please email amgrad@uwaterloo.ca or the student for the meeting link)

## Speaker

Jose Polo Gomez | Applied Mathematics, University of Waterloo

## Title

AQFT meets RQI: from foundations of quantum field theory to thermodynamics and machine learning

## Abstract

The fields of relativistic quantum information (RQI) and algebraic quantum field theory (AQFT) have proven to be successful in improving our understanding of several problems in quantum field theory, especially in curved backgrounds. In this talk, we will discuss a few lines of work in which combining the approaches and techniques of RQI and AQFT turns out to be fruitful.

First, we will consider the problem of measuring quantum fields. Representing measurements in quantum field theory in a way consistent with the fundamental framework of the theory (e.g., respecting relativity) has been a long-standing open foundational problem since Rafael Sorkin pointed it out in 1993. After reviewing previous approaches, we will show that a consistent measurement theory with arguably all the desirable properties can be built using particle detectors. To build this measurement scheme, we used an approach based on keeping track of where the information of the measurement is accessible, and we formalized this approach using the algebraic formalism. We will briefly discuss the prospective applications of this measurement framework, including the possibility of applying it to model measurements with so-called macrodetectors, based on work in preparation.

Second, we will consider the extension of thermodynamics to quantum field theory. We will review some of the conceptual difficulties of quantum thermodynamics, as well as the first works on its field-theoretical extension.

Finally, we will talk about studying the spacetime structure using quantum fields. It is well known that local correlations present in some distinguished states of quantum fields (such as the vacuum) contain information about the global structure of spacetime (its topology and geometry), and that this information can be harvested locally by particle detectors. This would allow targeting specific global parameters to be estimated by local measurements, and the use of machine learning techniques to optimize the information gathering protocol may allow us to take this idea to realistic setups.