“Physical Devices that Learn” (Prof. Florian Marquardt)

By designing devices that operate much closer to the microscopic laws of physics but offer similar functionalities, one can achieve significant gains in efficiency and speed. In this talk, I will give an overview of the different approaches. Above all, I will concentrate on the topic of physics-based learning, where the idea is to extract the correct updates to trainable parameters in an efficient manner, based on intelligently chosen experimental procedures. I will illustrate the opportunities in this domain by a few of our recent ideas. These include learning with the help of time-reversal operations, nonlinear processing based on linear optical scattering, and a new training technique that will help to turn any analogue quantum simulator platform into a trainable neuromorphic device.