Optimized higher-order photon state classification by machine learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913511251116032 |
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| author | Xu, Guangpeng Carvalho, Jeffrey Wijesundara, Chiran Thomay, Tim |
| author_facet | Xu, Guangpeng Carvalho, Jeffrey Wijesundara, Chiran Thomay, Tim |
| contents | The classification of higher-order photon emission becomes important with more methods being developed for deterministic multiphoton generation. The widely-used second-order correlation g(2) is not sufficient to determine the quantum purity of higher photon Fock states. Traditional characterization methods require a large amount of photon detection events which leads to increased measurement and computation time. Here, we demonstrate a Machine Learning model based on a 2D Convolutional Neural Network (CNN) for rapid classification of multiphoton Fock states up to |3> with an overall accuracy of 94%. By fitting the g(3) correlation with simulated photon detection events, the model exhibits efficient performance particularly with sparse correlation data, with 800 co-detection events to achieve an accuracy of 90%. Using the proposed experimental setup, this CNN classifier opens up the possibility for quasi real-time classification of higher photon states, which holds broad applications in quantum technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimized higher-order photon state classification by machine learning Xu, Guangpeng Carvalho, Jeffrey Wijesundara, Chiran Thomay, Tim Quantum Physics The classification of higher-order photon emission becomes important with more methods being developed for deterministic multiphoton generation. The widely-used second-order correlation g(2) is not sufficient to determine the quantum purity of higher photon Fock states. Traditional characterization methods require a large amount of photon detection events which leads to increased measurement and computation time. Here, we demonstrate a Machine Learning model based on a 2D Convolutional Neural Network (CNN) for rapid classification of multiphoton Fock states up to |3> with an overall accuracy of 94%. By fitting the g(3) correlation with simulated photon detection events, the model exhibits efficient performance particularly with sparse correlation data, with 800 co-detection events to achieve an accuracy of 90%. Using the proposed experimental setup, this CNN classifier opens up the possibility for quasi real-time classification of higher photon states, which holds broad applications in quantum technologies. |
| title | Optimized higher-order photon state classification by machine learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2404.16203 |