SKM 2023 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

DY: Fachverband Dynamik und Statistische Physik

DY 22: Machine Learning in Dynamics and Statistical Physics II

DY 22.1: Vortrag

Dienstag, 28. März 2023, 14:00–14:15, ZEU 160

Reservoir Computing using Quantum Dot Lasers — •Huifang Dong, Lina Jaurigue, and Kathy Lüdge — Institute of Physik, Technische Universität Ilmenau, Weimarer Str. 32, 98684 Ilmenau, Germany.

Time-multiplexed reservoir computing is a machine-learning approach which is well suited for implementation using semiconductor lasers subject to optical feedback. In such a delay-based setup the feedback has two important roles; it directly influences the memory of the system and it generates the high dimensional transient dynamics needed for good computational performance [1]. However, commonly used and commercially available quantum well semiconductor lasers are dynamically very sensitive to optical feedback, which can make the implementation of such systems difficult. Implementation and on-chip integration of optical reservoir computing become feasible with quantum dot lasers, as they emit at the telecommunication wavelength and are less sensitive to unwanted reflections [2]. Using typical benchmark tasks for time series prediction we show that quantum dot lasers show good computing performance that can be further optimized by proper delay time tuning.

[1] T. Hülser, et al., Opt. Mater. Express 12, 3, 1214 (2022).

[2] C. Otto, et al., Int. J. Bifurc. Chaos 22, 10, 1250246 (2012).