Scheme: University Research Fellowship
Organisation: University of Birmingham
Dates: Oct 2013-Sep 2018
Summary: Understanding matter requires realising that "more is different": many particles together can lead to fascinating phenomena defying intuition based on the individual constituents. Such emergent phenomena, arising from the collective quantum physics of many particles, is the central theme of my research.
I explore two main directions: the first of these studies a new family of insulating systems called "fractional topological insulators". Here, the collective dance of many electrons leads to emergent particles seemingly breaking electrons into smaller pieces, while at the same time it transforms the sample boundaries into an excellent conductor. Currently we barely know more than that these systems can exist. My research aims to understand their possible forms and the mechanisms which allow their formation, and to predict experimentally observable phenomena which will help identifying them in the laboratory.
The second direction focuses on another way of breaking up electrons, this time into seemingly neutral particles called Majorana fermions. These entities can arise in "topological superconductors" which turn out to provide just the right environment for quantum mechanics to hide the electric charge. The questions I am interested in concern the way these particles interact with their electronic environment, be this an experimental probe or the circuitry of a Majorana based device. Understanding this interaction can not only lead to predictions of new emergent phenomena, but is also crucial for understanding ongoing Majorana experiments.
These two strands of "topological matter" hide a wealth of unexplored fundamental and exotic phenomena. But beyond this, topological matter might also underpin new technology: the excellent conduction properties of topological insulator surfaces might lead to the birth of topological electronics, while Majorana fermions are predicted to be potential resources in quantum information processing.