Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908385871396864 |
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| author | Li, Zhenghao Kendall, Matthew J. H. Machado, Gerard J. Zhu, Ruidi Mer, Ewan Zhan, Hao Zhang, Aonan Yu, Shang Walmsley, Ian A. Patel, Raj B. |
| author_facet | Li, Zhenghao Kendall, Matthew J. H. Machado, Gerard J. Zhu, Ruidi Mer, Ewan Zhan, Hao Zhang, Aonan Yu, Shang Walmsley, Ian A. Patel, Raj B. |
| contents | Transition-Edge Sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared to leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies which demand fast active feed-forward of PNR detection outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning Li, Zhenghao Kendall, Matthew J. H. Machado, Gerard J. Zhu, Ruidi Mer, Ewan Zhan, Hao Zhang, Aonan Yu, Shang Walmsley, Ian A. Patel, Raj B. Quantum Physics Instrumentation and Detectors Transition-Edge Sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared to leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies which demand fast active feed-forward of PNR detection outcomes. |
| title | Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning |
| topic | Quantum Physics Instrumentation and Detectors |
| url | https://arxiv.org/abs/2411.15360 |