  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
paper, we show how this approach holds promise for adaptive measurement
strategies to characterize present-day quantum technology 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.
