In this section
@article{Li:2025:10.1364/opticaq.555325,
author = {Li, Z and Kendall, MJH and Machado, GJ and Zhu, R and Mer, E and Zhan, H and Zhang, A and Yu, S and Walmsley, IA and Patel, RB},
doi = {10.1364/opticaq.555325},
journal = {Optica Quantum},
pages = {246--246},
title = {Boosting photon-number-resolved detection rates of transition-edge sensors by machine learning},
url = {http://dx.doi.org/10.1364/opticaq.555325},
volume = {3},
year = {2025}
}
TY - JOUR
AB - 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 with 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 that demand fast active feed-forward of PNR detection outcomes.
AU - Li,Z
AU - Kendall,MJH
AU - Machado,GJ
AU - Zhu,R
AU - Mer,E
AU - Zhan,H
AU - Zhang,A
AU - Yu,S
AU - Walmsley,IA
AU - Patel,RB
DO - 10.1364/opticaq.555325
EP - 246
PY - 2025///
SN - 2837-6714
SP - 246
TI - Boosting photon-number-resolved detection rates of transition-edge sensors by machine learning
T2 - Optica Quantum
UR - http://dx.doi.org/10.1364/opticaq.555325
UR - https://doi.org/10.1364/opticaq.555325
VL - 3
ER -