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.

翻译：贝叶斯实验设计是一种通过最大化期望信息增益来高效选择测量方案以表征物理系统的技术。近年来深度神经网络与归一化流的发展，使得后验概率的近似计算更加高效，从而将该技术扩展到复杂高维场景。本文展示了该方法如何为当前量子技术平台的自适应测量策略提供可行方案。我们重点研究耦合腔阵列与量子比特阵列：两者都是量子模拟与量子计算等现代应用领域高度相关的模型系统，且均已在实际平台中实现——在这些平台上，测量与控制技术可用于表征并补偿不可避免的无序性。因此，它们构成了贝叶斯实验设计应用的理想目标。