Autonomous Optical Alignment of Satellite-Based Entanglement Sources using Reinforcement Learning
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Abstract
Quantum entanglement distributed via satellites enable global-scale quantum communication. However, onboard sources are susceptible to misalignment due to dynamical orbital conditions. Here, we present two recalibration techniques for efficient generation of high quality entanglement using a periodically poled lithium niobate (PPLN)-based spontaneous parametric down-conversion (SPDC) source with minimum intervention. The first is a heuristic algorithm (HA) which mimics the manual alignment process in a laboratory. The second is based on reinforcement learning (RL). Our simulation demonstrates superior performance of RL with AUC=0.9119 compared to HA's 0.7042 in the modified ROC analysis (60 min threshold). RL achieves perfect alignment in 10 min as opposed to HA's 30 min. Both the methods operate within feasible satellite constraints, offering scalable automation for complex quantum communication scenarios.