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Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

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Cross-predicting the dynamics of an optically injected single-mode semiconductor laser using reservoir computing.

A Cunillera¹, M C Soriano¹, I Fischer¹

¹Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.

Chaos (Woodbury, N.Y.)

|November 30, 2019

Summary

This study uses reservoir computing to reconstruct missing data in chaotic laser systems. The machine learning model accurately predicts unmeasured laser dynamics from limited information, aiding system monitoring and control.

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Area of Science:

Physics
Engineering
Computer Science

Background:

Real-world dynamical systems often have incomplete data due to technical limitations.
This data gap hinders system monitoring, control, and decision-making.
Cross-predicting missing variables is crucial for understanding and managing these systems.

Purpose of the Study:

To apply a machine learning algorithm for cross-predicting unknown variables in a chaotic dynamical laser system.
To reconstruct the dynamics of unmeasured variables using limited available data.
To assess the accuracy and robustness of the proposed method.

Main Methods:

Utilized a machine learning algorithm based on reservoir computing.
Applied the algorithm to a realistic model of an optically injected single-mode semiconductor laser.
Trained the algorithm with partial data and tested its predictive capabilities on unmeasured variables (electric field phase and carrier dynamics) from laser intensity.

Main Results:

Demonstrated accurate reconstruction of two unmeasured dynamical variables from a single measured variable (laser intensity).
Analyzed the impact of laser system and reservoir parameters on prediction accuracy.
Validated the robustness of the cross-prediction method against noisy time series data.

Conclusions:

Reservoir computing effectively reconstructs missing dynamical variables in chaotic laser systems.
The developed method shows potential for time series reconstruction, data recovery, and secure data encryption applications.
This approach offers a powerful tool for analyzing and controlling complex dynamical systems with limited observability.