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QI: Fachverband Quanteninformation

QI 36: Poster – Quantum Information (joint session QI/Q)

QI 36.55: Poster

Donnerstag, 13. März 2025, 17:00–19:00, Tent

Complexity: chaos, regular, and complex — •Adisorn Panasawatwong, Jan-Michael Rost, and Ulf Saalmann — MPI-PKS

We are developing a machine learning-based approach to extract meaningful information from noisy physical observables. Distinguishing signal from noise in chaotic systems is a significant challenge. Our primary goal is to introduce a novel method for quantifying the inherent complexity of these signals, similar to resolution functions used in standard data analysis. A key aspect of our approach is to assign zero complexity to systems that exhibit either extreme regularity or extreme chaos. We designed machine learning networks specifically tailored to uncover hidden patterns within these noisy observables. This approach aims to enhance our ability to extract critical information from a wide range of applications, from classical noise to the complex quantum systems that produce noisy, intricate data sets.

Keywords: Time series; Chaos; Complexity; Machine learning; Information