Related papers: PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

URL: http://arxiv.org/abs/2509.26315v3
Date: Fri, 10 Oct 2025 23:32:48 GMT
Title: PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination
Authors: Karl C. Linne, Sho Uemura, Yue Ji, Allen Zang, Ian Chin, Martin Di Federico, Gustavo Cancelo, Orlando Quaranta, Debashri Roy, Tian Zhong,
Abstract summary: We introduce PhotonIDs, a machine learning-powered photon identification system.<n>PhotonIDs is the first end-to-end solution for real-time discrimination between photons and dark count.<n>Our result demonstrates that PhotonIDs could improve more than 31.2 times of signal-noise-ratio(SNR) on dark count elimination.
Score: 3.889081194831016
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

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