Deep learning control of THz QCLs.

Area of Science:

• Physics
• Quantum Optics
• Machine Learning

Background:

• Complex, non-linear systems are prevalent in nature, notably in optical mode interactions within laser resonators.
• Quantum Cascade Random Lasers exhibit intricate non-linear dynamics that are challenging to model and control.

Purpose of the Study:

• To apply artificial neural networks (ANNs) for modeling complex optical mode interactions within a Quantum Cascade Random Laser cavity.
• To develop a real-time method for predicting laser spectra modulation schemes.

Main Methods:

• Training ANNs to learn the non-linear interactions within the laser cavity.
• Utilizing the trained ANNs to predict modulation schemes for achieving specific laser output spectra.

Main Results:

• Demonstrated the capability of ANNs to accurately model complex non-linear optical interactions.
• Achieved real-time prediction of modulation schemes for desired laser spectra.
• Showcased the potential for adapting laser spectra to specific requirements.

Conclusions:

• ANNs offer a powerful and efficient tool for understanding and controlling complex systems like Quantum Cascade Random Lasers.
• This approach significantly reduces the need for extensive simulations and physical prototyping.
• Enables rapid, customized spectral output for lasers, opening new avenues in optical engineering.