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

• URL: http://arxiv.org/abs/2405.05243v1
• Date: Wed, 8 May 2024 17:40:03 GMT
• Title: Deep learning-based variational autoencoder for classification of quantum and classical states of light
• Authors: Mahesh Bhupati, Abhishek Mall, Anshuman Kumar, Pankaj K. Jha,
• Abstract summary: We introduce a deep learning-based variational autoencoder (VAE) for classifying single photon added coherent state (SPACS) VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. We envision that such a deep learning methodology will enable better classification of quantum light and light sources even in the presence of poor detection quality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 thermal/SPATS of light. Our semisupervised learning-based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non-unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. We envision that such a deep learning methodology will enable better classification of quantum light and light sources even in the presence of poor detection quality.

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