Boosting photon-number-resolved detection rates of transition-edge sensors by machine learning
File(s)
Published version
Author(s)
Type
Journal Article
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 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.
Date Issued
2025-06-25
Date Acceptance
2025-05-12
Citation
Optica Quantum, 2025, 3 (3), pp.246-246
ISSN
2837-6714
Publisher
Optica Publishing Group
Start Page
246
End Page
246
Journal / Book Title
Optica Quantum
Volume
3
Issue
3
Copyright Statement
Journal © 2025 Optica Publishing Group. Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
License URL
Identifier
10.1364/OPTICAQ.555325
Publication Status
Published
Date Publish Online
2025-05-29