Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light
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 requires efficient detectors an...
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| Language: | English |
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Wiley-VCH
2025-02-01
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| Series: | Advanced Physics Research |
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| Online Access: | https://doi.org/10.1002/apxr.202400089 |
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| author | Mahesh Bhupati Abhishek Mall Anshuman Kumar Pankaj K. Jha |
| author_facet | Mahesh Bhupati Abhishek Mall Anshuman Kumar Pankaj K. Jha |
| author_sort | Mahesh Bhupati |
| collection | DOAJ |
| description | 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 requires efficient detectors and longer measurement times to obtain high‐quality photon statistics. Here, a deep learning‐based variational autoencoder (VAE) method is introduced for classifying single photon added coherent state (SPACS), single photon added thermal state (SPATS), and mixed states between coherent and SPACS as well as between thermal and SPATS of light. The semi‐supervised 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. It is envisioned that such a deep learning methodology will enable better classification of quantum light and light sources even in poor detection. |
| format | Article |
| id | doaj-art-77210cb8b75544f8bb14cb3c6c52063f |
| institution | OA Journals |
| issn | 2751-1200 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Physics Research |
| spelling | doaj-art-77210cb8b75544f8bb14cb3c6c52063f2025-08-20T01:57:35ZengWiley-VCHAdvanced Physics Research2751-12002025-02-0142n/an/a10.1002/apxr.202400089Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of LightMahesh Bhupati0Abhishek Mall1Anshuman Kumar2Pankaj K. Jha3Laboratory of Optics of Quantum Materials (LOQM) Department of Physics Indian Institute of Technology Bombay Powai Mumbai 400076 IndiaMax Planck Institute for the Structure and Dynamics of Matter Hamburg 22761 GermanyLaboratory of Optics of Quantum Materials (LOQM) Department of Physics Indian Institute of Technology Bombay Powai Mumbai 400076 IndiaQuantum Technology Laboratory 〈Q|T|L〉 Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY 13210 USAAbstract 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 requires efficient detectors and longer measurement times to obtain high‐quality photon statistics. Here, a deep learning‐based variational autoencoder (VAE) method is introduced for classifying single photon added coherent state (SPACS), single photon added thermal state (SPATS), and mixed states between coherent and SPACS as well as between thermal and SPATS of light. The semi‐supervised 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. It is envisioned that such a deep learning methodology will enable better classification of quantum light and light sources even in poor detection.https://doi.org/10.1002/apxr.202400089deep learningspacsspatsvariational autoencoder |
| spellingShingle | Mahesh Bhupati Abhishek Mall Anshuman Kumar Pankaj K. Jha Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light Advanced Physics Research deep learning spacs spats variational autoencoder |
| title | Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light |
| title_full | Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light |
| title_fullStr | Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light |
| title_full_unstemmed | Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light |
| title_short | Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light |
| title_sort | deep learning based variational autoencoder for classification of quantum and classical states of light |
| topic | deep learning spacs spats variational autoencoder |
| url | https://doi.org/10.1002/apxr.202400089 |
| work_keys_str_mv | AT maheshbhupati deeplearningbasedvariationalautoencoderforclassificationofquantumandclassicalstatesoflight AT abhishekmall deeplearningbasedvariationalautoencoderforclassificationofquantumandclassicalstatesoflight AT anshumankumar deeplearningbasedvariationalautoencoderforclassificationofquantumandclassicalstatesoflight AT pankajkjha deeplearningbasedvariationalautoencoderforclassificationofquantumandclassicalstatesoflight |