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Conference on Lasers and Electro-Optics

Quantum Few-body Dynamics of Rydberg Atom Clusters

Woojun Lee, Minhyuk Kim, Hanlae Jo, Yunheung Song, Jaewook Ahn

Published Jan 1, 2019• 0 citations
Publisher

Abstract

We model and experimentally verify the quantum dynamics of small-scale Rydberg-atom quantum simulators, or entangled Rydberg atom clusters, using Lindblad equations with control parameters including spontaneous emission, stochastic atom loss, and laser phase noise.
DOI
10.1364/cleo_qels.2019.fm2a.7

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One of the most fascinating and challenging topics of recent years is many- body physics. Owing to the large number of components of real physi- cal systems an analytical approach or numerical solutions are typically not practicable. However, an experimental approach to this problem is possible thanks to an unprecedented level of matter control on the atomic scale. In our setup we use atoms excited to high-lying energy states, called Rydberg atoms, which have peculiar properties like the extremely long lifetime and strong interactions. These experimental features make a cold ensem- ble of Rydberg atoms a very controllable example of a strongly interacting many-body system and provide a promising framework for quantum simulation.
Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.