Time series learning in a many-body Rydberg system with emergent collective nonlinearity

TL;DR

This paper demonstrates that a many-body Rydberg system near a phase transition can be used for improved time series prediction, leveraging emergent collective nonlinearity to enhance forecasting accuracy.

Contribution

It introduces a novel application of Rydberg vapors for time series prediction, exploiting collective effects near phase transitions for enhanced data processing.

Findings

Prediction accuracy increases near phase transition

Emergent nonlinearity improves forecasting capabilities

Rydberg systems can process noisy data effectively

Abstract

Interacting Rydberg atoms constitute a versatile platform for the realization of non-equilibrium states of matter. Close to phase transitions, they respond collectively to external perturbations, which can be harnessed for technological applications in the domain of quantum metrology and sensing. Owing to the controllable complexity and straightforward interpretability of Rydberg atoms, we can observe and tune the emergent collective nonlinearity. Here, we investigate the application of an interacting Rydberg vapour for the purpose of time series prediction. The vapour is driven by a laser field whose Rabi frequency is modulated in order to input the time series. We find that close to a non-equilibrium phase transition, where collective effects are amplified, the capability of the system to learn the input becomes enhanced. This is reflected in an increase of the accuracy with which future values of the time series can be predicted. Using the Lorenz time series and temperature data as examples, our work demonstrates how emergent phenomena enhance the capability of noisy many-body quantum systems for data processing and forecasting.