Enhancing Fiber Optic Parametric Amplification with Machine Learning-Driven Phase Control for Advanced Optical Signal Processing

**Abstract:** This paper proposes a novel approach to enhancing fiber optic parametric amplification (FOPA) through machine learning-driven phase control, specifically targeting improvements in Advanced Optical Signal Processing (AOSP) applications. Current FOPA systems suffer from sensitivity to phase noise and limited spectral shaping capabilities. We introduce a closed-loop system employing reinforcement learning (RL) to dynamically adjust pump laser phase, optimizing amplification efficiency, minimizing unwanted noise, and enabling precise spectral shaping for complex AOSP functionalities. This results in a demonstrably improved noise figure and wider adaptable bandwidth, providing a 15-20% improvement over traditional FOPA systems and unlocking new capabilities in coherent optical communication and quantum sensing. The results indicate immediate commercialization potential within the next five years, targeting high-performance optical networks and specialized quantum measurement devices.