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Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

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Parallel and deep reservoir computing using semiconductor lasers with optical feedback.

Hiroshi Hasegawa¹, Kazutaka Kanno¹, Atsushi Uchida¹

¹Department of Information and Computer Sciences, Saitama University, 255 Shimo-okubo, Sakura-ku, Saitama City, Saitama 338-8570, Japan.

Nanophotonics (Berlin, Germany)

|December 5, 2024

Summary

Multiple photonic reservoirs improve machine learning tasks. Hybrid configurations outperform single, parallel, and deep setups for chaotic prediction and channel equalization, boosting edge computing potential.

Keywords:

deep learning machine learning semiconductor laser time delay

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Area of Science:

Optics and Photonics
Machine Learning
Nonlinear Dynamics

Background:

Photonic reservoir computing (PRC) offers efficient machine learning solutions.
Limited training of connection weights in single reservoirs restricts performance.
Multiple reservoirs can overcome these limitations.

Purpose of the Study:

Investigate parallel and deep configurations of delay-based all-optical PRC.
Propose a hybrid configuration combining parallel and deep approaches.
Enhance PRC performance for complex computational tasks.

Main Methods:

Utilized semiconductor lasers with optical feedback for all-optical PRC.
Implemented parallel and deep reservoir configurations.
Evaluated performance on chaotic time-series prediction, nonlinear channel equalization, and memory capacity tasks.

Main Results:

Deep reservoirs excel at chaotic time-series prediction.
Parallel reservoirs are optimal for nonlinear channel equalization.
Hybrid reservoirs demonstrated superior performance across all tested tasks.

Conclusions:

Multiple reservoir configurations significantly improve PRC performance.
Hybrid configurations offer the most robust and versatile solution.
Optimized multiple reservoirs show promise for high-performance edge computing applications.