Recent years have seen the onslaught of new and exciting computational methods to both assess and compress the rich informational structure in quantum many-body systems, including neural networks, autoencoders, and dimensional reduction techniques. One continuing challenge is to robustly quantify long-range behavior in quantum systems vis-à-vis measures beyond correlation functions. Persistent homology, a unique computational tool central to topological data analysis, is well-suited to this task. In this talk I will give an overview and example of persistent homology, as well as detail recent research that used persistent homology to identify quantum and classical phase transitions.
The Zoom link will be sent to the Graduate Student and PDRA mailing lists. If you are not on one of those lists and are interested in attending, please email Mark Hirsbrunner at hrsbrnn2@illinois.edu for the link.
