Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning

• URL: http://arxiv.org/abs/2411.15360v1
• Date: Fri, 22 Nov 2024 22:09:50 GMT
• Title: Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning
• Authors: Zhenghao Li, Matthew J. H. Kendall, Gerard J. Machado, Ruidi Zhu, Ewan Mer, Hao Zhan, Aonan Zhang, Shang Yu, Ian A. Walmsley, Raj B. Patel,
• Abstract summary: Transition-Edge Sensors (TESs) are very effective photon-number-resolving (PNR) detectors. Their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios. 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.
• Score: 2.9829310103458186
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• Abstract: 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.

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