Advanced Physics Research · 2024
Deep Learning–Based Variational Autoencoder for
Classification of Quantum & Classical States of Light
Authors
Mahesh Bhupati*, Abhishek Mall*, Anshuman Kumar, and Pankaj K. Jha
Abstract
Advancements in optical quantum technologies hinge on generation, manipulation, and characterization of light via photon statistics. Traditional classification requires efficient detectors and long acquisition times. Here, we introduce a semi-supervised variational autoencoder (VAE) that maps photon-statistics features of SPACS, SPATS, and mixed states into a low-dimensional latent space for rapid classification at low photon counts. The VAE remains robust against experimental losses—limited quantum efficiencies, detector imperfections—and uses transfer learning to classify unseen datasets without retraining. This framework enables fast, accurate identification of quantum light sources even under poor detection conditions.