Deep learning-based variational autoencoder for classification of quantum and classical states of light

arXiv:2405.05243v1 Announce Type: cross
Abstract: Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements often requires efficient detectors and longer measurement times to obtain high-quality photon statistics. Here we introduce a deep learning-based variational autoencoder (VAE) method for classifying single photon added coherent state (SPACS), single photon added thermal state (SPACS), mixed states between coherent/SPACS and …

abstract arxiv autoencoder classification cs.lg deep learning detectors however identification light manipulation measurement optical photon physics.comp-ph quant-ph quantum statistics technologies through type