Spatial Mode Correction of Single Photons Using Machine Learning

Spatial modes of light constitute valuable resources for a variety quantum technologies ranging from communication and imaging to remote sensing. Nevertheless, their vulnerabilities phase distortions, induced by random media, impose significant limitations on the realistic implementation numerous quantum-photonic technologies. Unfortunately, this problem is exacerbated at single-photon level. Over last two decades, challenging has been tackled through conventional schemes that utilize optical nonlinearities, correlations, adaptive optics. In article, self-learning self-evolving features artificial neural networks are exploited correct complex spatial profile distorted Laguerre–Gaussian Furthermore, potential technique used improve channel capacity an protocol relies structured single photons. The results have important implications real-time turbulence correction photons images.