Accurate Unsupervised Photon Counting from Transition Edge Sensor Signals

Published

September 5, 2025

Author(s)

Nicolas Dalbec-Constant, Guillaume Thekkadath, Duncan England, Thomas Gerrits, Nicolas Quesada

Abstract

We compare methods for signal classification applied to voltage traces from transition edge sensors (TES) which are photon-number resolving detectors fundamental for accessing quantum advantages in information processing, communication and metrology. We quantify the impact of numerical analysis on the distinction of such signals. Furthermore, we explore dimensionality reduction techniques to create interpretable and precise photon number embeddings. We demonstrate that the preservation of local data structures of some nonlinear methods is an accurate way to achieve unsupervised classification of TES traces. We do so by considering the Confidence that quantifies the overlap of the signal's probability distribution inside an embedding. We demonstrate that for our dataset previous methods like the signal's area and principal component analysis can resolve up to 16 photons with Confidence above 90% while nonlinear can resolve up to 21 with the same confidence threshold. We also showcase implementations of neural networks to leverage information within local structures, aiming to increase confidence in assigning photon numbers. Finally, we demonstrate the advantage of some nonlinear methods to detect and remove outlier signals.

Citation

Quantum Science and Technology

Pub Type

Journals

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Keywords

photon-number resolution, transition edge sensor, quantum optics

Citation

Dalbec-Constant, N. , Thekkadath, G. , England, D. , Gerrits, T. and Quesada, N. (2025), Accurate Unsupervised Photon Counting from Transition Edge Sensor Signals, Quantum Science and Technology, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958777 (Accessed November 20, 2025)