Systems and methods for tuning optical cavities using machine learning techniques

An optical system, comprising:

• an optical cavity;
• at least one processor coupled to the optical cavity;
• and
• at least one computer-readable storage medium storing computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to carry out a method comprising:
• analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from the optical cavity to determine a degree of misalignment, wherein determining the degree of misalignment comprises using the CNN model to determine a difference between the measurement signal and a spatial profile image comprising a Gaussian zero-order mode;
• determining, using a reinforcement learning (RL) model, a tuning parameter based on the degree of misalignment;
• and
• tuning the optical cavity using the tuning parameter.

At least one computer-readable storage medium encoded with computer-executable instructions that, when executed by a computer, cause the computer to carry out a method comprising:

• analyzing, using a convolutional neural network (CNN) model, a measurement signal obtained from an optical cavity to determine a degree of misalignment, wherein determining the degree of misalignment comprises using the CNN model to determine a difference between the measurement signal and a spatial profile image comprising a Gaussian zero-order mode;
• determining, using a reinforcement learning (RL) model, a tuning parameter based on the degree of misalignment;
• and
• tuning the optical cavity using the tuning parameter.