Deep Bayesian Experimental Design for Quantum Many-Body Systems

TL;DR

This paper explores the use of deep Bayesian experimental design with neural networks to efficiently optimize measurements for characterizing complex quantum many-body systems like coupled cavities and qubit arrays.

Contribution

It demonstrates how deep learning techniques can extend Bayesian experimental design to high-dimensional quantum systems for adaptive measurement strategies.

Findings

Effective measurement strategies for quantum arrays

Enhanced characterization of quantum systems

Potential for improved quantum simulation and computing

Abstract

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.