Time‐Multiplexed Reservoir Computing with Quantum‐Dot Lasers: Impact of Charge‐Carrier Scattering Timescale

Reservoir computing with optical devices offers an energy‐efficient approach for time‐series forecasting. Quantum dot lasers with feedback are modeled in this article to explore the extent to which increased complexity in the charge‐carrier dynamics within the nanostructured semiconductor can enhance the prediction performance. By tuning the scattering interactions, the laser's dynamics and response time can be finely adjusted, allowing for a systematic investigation. It is found that both system response time and task requirements need to be considered to find optimal operation conditions. Further, lasers with pronounced relaxation oscillations outperform those with strongly damped dynamics, even if the underlying charge‐carrier dynamics is more complex. This demonstrates that optimal reservoir computing performance relies not only on a high internal phase space dimension but also on the effective utilization of these dynamics through the output sampling process, quantum dot laser, reservoir computing, feedback delay, effective scattering rate, relaxation oscillation.

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Publication Details

Journal
physica status solidi (RRL) - Rapid Research Letters
Published
2025-02-03
DOI
https://doi.org/10.1002/pssr.202400433
Citations
6
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
14.89

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article

Time‐Multiplexed Reservoir Computing with Quantum‐Dot Lasers: Impact of Charge‐Carrier Scattering Timescale

Kathy Lüdge, Lina Jaurigue, Huifang Dong
6 citations
physica status solidi (RRL) - Rapid Research Letters
Neural Networks and Reservoir Computing
14.89
article

Time‐Multiplexed Reservoir Computing with Quantum‐Dot Lasers: Impact of Charge‐Carrier Scattering Timescale

Kathy Lüdge, Lina Jaurigue, Huifang Dong
article en
6 citations

Abstract

Reservoir computing with optical devices offers an energy‐efficient approach for time‐series forecasting. Quantum dot lasers with feedback are modeled in this article to explore the extent to which increased complexity in the charge‐carrier dynamics within the nanostructured semiconductor can enhance the prediction performance. By tuning the scattering interactions, the laser's dynamics and response time can be finely adjusted, allowing for a systematic investigation. It is found that both system response time and task requirements need to be considered to find optimal operation conditions. Further, lasers with pronounced relaxation oscillations outperform those with strongly damped dynamics, even if the underlying charge‐carrier dynamics is more complex. This demonstrates that optimal reservoir computing performance relies not only on a high internal phase space dimension but also on the effective utilization of these dynamics through the output sampling process, quantum dot laser, reservoir computing, feedback delay, effective scattering rate, relaxation oscillation.

physica status solidi (RRL) - Rapid Research LettersVol. 19(6)
Technische Universität Ilmenau (DE)
Carl-Zeiss-Stiftung, European Commission
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
14.89
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