AI Boosts Optical Cavity Performance

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Aug 1 2024

In a recent article published in the journal Machine Learning: Science and Technology, researchers introduced an innovative method to lock a laser to an optical cavity using reinforcement learning (RL), a branch of artificial intelligence (AI) that learns from its actions and rewards. This approach aims to enhance the performance and reliability of optical systems.

Optical Cavities and the Pound-Drever-Hall Technique

An optical cavity consists of two or more mirrors reflecting light back and forth, creating a standing wave pattern. They are essential in high-precision light measurements, such as gravitational wave detection, quantum optics, and laser spectroscopy. For optimal performance, the laser light wavelength entering the cavity must match the cavity length, ensuring resonance. Resonance allows light to build up inside the cavity, increasing its intensity and sensitivity. However, maintaining resonance is challenging due to environmental factors like temperature, pressure, and vibration.

The Pound-Drever-Hall (PDH) is a common method to lock a laser into a cavity. It uses a phase modulator to imprint sidebands on the laser beam at a fixed frequency. The beam reflects off the cavity and is detected by a photodetector, producing an error signal indicating deviation from resonance. This error signal adjusts the laser wavelength to restore the lock.

The PDH technique involves two types of feedback control: a fast, high-bandwidth control using a piezoelectric transducer to change the laser crystal shape and a slow, low-bandwidth control changing the laser crystal temperature. Fast control suppresses most environmental noises but has a limited dynamic range, while slow control compensates for long-term drifts but requires a frequency-domain filter to avoid instability.

Q-Learning for Optical-Cavity Locking

In this paper, the authors proposed a novel approach to replace the frequency-domain filter with a Q-Learning agent, an RL algorithm that learns the optimal actions for different environmental states. The Q-Learning agent uses the Red Pitaya, a digitizer board that interfaces with the laser system, to control the slow feedback output that regulates the laser crystal temperature.

The Q-Learning agent takes two inputs: the error signal from the PDH technique (used as the state) and the voltage from the cavity transmission photodetector (used to check the lock status and reward the agent during training). The agent has five possible actions, changing the temperature by small increments from -0.001 V to 0.001 V. It learns a Q-matrix that stores the expected future reward for each state-action pair and updates it iteratively based on observed outcomes. The agent follows an ε-greedy policy, which means it chooses the action with the highest Q-value most of the time but occasionally explores a random action to discover new possibilities.

Furthermore, the Q-Learning agent was trained for 5000 episodes, each lasting from the moment the system acquired lock to the moment it lost it. The agent received a reward of 1 for each time the lock was maintained and 0 otherwise. The training took about five days, during which the agent improved its Q-matrix in real-time while the laser was active.

Findings and Applications

The researchers tested the Q-learning agent using a purely greedy strategy, always choosing the action with the highest Q-value. The agent showed remarkable performance, maintaining the lock for much longer than the baseline method, which kept the temperature fixed after the PDH lock. The baseline method achieved an average lock duration of 34 minutes, with a maximum of 124 minutes. The Q-Learning method maintained an average lock duration of eight days, with a maximum of 12 days.

The agent also exhibited smart behavior, steering the error signal toward the optimal operating range and avoiding lock-loss states. It learned to adapt to changing conditions and balance exploration and exploitation. The agent used a specific action, corresponding to a 2.5 mV change, selectively when the error signal fell within a certain range, demonstrating a nuanced response to specific signal conditions.

The Q-Learning method for optical-cavity locking has significant implications for high-sensitivity physics experiments relying on stable and precise light sources. This method can enhance the performance and reliability of optical systems, such as those used for gravitational wave detection, quantum optics, and laser spectroscopy. It can also reduce the need for manual tuning and calibration, enabling real-time learning and adaptation.

Conclusion

In summary, the novel technique proved effective for locking a laser into an optical cavity, significantly improving the lock's duration and stability compared to conventional techniques. It demonstrated smart and adaptive behavior, steering the error signal toward the optimal operating range and avoiding lock-loss states.

Moving forward, the researchers suggested improving their method by incorporating more features and parameters into the state representation, such as cavity length, laser power, and environmental conditions. They also proposed extending their method to other types of optical systems, such as ring cavities, optical lattices, and optical tweezers.