Deep Bayesian Experimental Design for Quantum Many-Body Systems

Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system, by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of this technique to complex high-dimensional situations. In this talk, we show how this approach holds promise for adaptive measurement strategies to characterize present-day quantum many-body platforms. In particular, we focus on arrays of coupled cavities and qubit arrays. Both represent model systems of high relevance for modern applications, like quantum simulations and computing, and both have been realized in platforms where measurement and control can be exploited to characterize and counteract unavoidable disorder. Thus, they represent ideal targets for applications of Bayesian Experimental Design.