Autonomous Optical Alignment of Satellite-Based Entanglement Sources using Reinforcement Learning

TL;DR

This paper introduces reinforcement learning-based techniques for autonomous optical alignment of satellite-based entanglement sources, significantly improving efficiency and reducing intervention time in quantum communication systems.

Contribution

It presents a novel RL approach for satellite source alignment, outperforming heuristic algorithms in speed and accuracy, within practical satellite constraints.

Findings

RL achieves perfect alignment in 10 minutes

RL outperforms heuristic algorithm in ROC AUC

Both methods are feasible for satellite implementation

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.