Deep Bayesian experimental design for quantum many-body systems

Sarra L, Marquardt F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Machine Learning: Science and Technology Institute of Physics Publishing (IOP)

Book Volume: 4

Article Number: 045022

Journal Issue: 4

DOI: 10.1088/2632-2153/ad020d

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.

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Sarra, L., & Marquardt, F. (2023). Deep Bayesian experimental design for quantum many-body systems. Machine Learning: Science and Technology, 4(4). https://doi.org/10.1088/2632-2153/ad020d

MLA:

Sarra, Leopoldo, and Florian Marquardt. "Deep Bayesian experimental design for quantum many-body systems." Machine Learning: Science and Technology 4.4 (2023).

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