Deep Bayesian Experimental Design for Quantum Many-Body Systems
This work addresses the challenge of characterizing complex quantum systems for applications in quantum simulations and computing, representing an incremental advancement in measurement optimization.
The paper tackles the problem of efficiently characterizing quantum many-body systems by applying deep Bayesian experimental design to select optimal measurements, demonstrating its potential for adaptive strategies in quantum technology platforms like coupled cavities and qubit arrays.
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.