In particular, we are following two main lines of research. One of our aims is to understand the role of topology in systems exhibiting gain and loss and how these systems can be harnessed to devise quantum devices such as quantum-limited directional amplifiers and sensors. Specifically, we have shown that non-Hermitian topology corresponds one-to-one with the phenomenon of directional amplification, which is highly sought-after for applications including quantum information processing.

In another line of research, we are investigating energy-efficient neuromorphic hardware approaches to machine learning based on optics and photonics with the goal of developing a framework for replacing digital neural networks with physical systems performing the necessary computations. For instance, we recently proposed a framework for non-linear neuromorphic computing based on purely linear scattering. You can learn more about our work here.