Boosting Photon-Number-Resolved Detection Rates of Transition-Edge Sensors by Machine Learning
arXiv:2411.15360 · doi:10.1364/OPTICAQ.555325
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.
18 pages, 7 figures including supplimental material
References in corpus (11)
- Generation of Optical Coherent State Superpositions by Number-Resolved Photon Subtraction from Squeezed Vacuum
- Entanglement-enhanced measurement of a completely unknown phase
- Bake off redux: a review and experimental evaluation of recent time series classification algorithms
- Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage
- Quantum metrology with imperfect states and detectors
- Mapping coherence in measurement via full quantum tomography of a hybrid optical detector
- A Review of X-ray Microcalorimeters Based on Superconducting Transition Edge Sensors for Astrophysics and Particle Physics
- Non-Gaussian quantum state generation by multi-photon subtraction at the telecommunication wavelength
- Reducing of a parametric down-conversion source via photon-number resolution with superconducting nanowire detectors
- Enhanced Detection Rate and High Photon-Number Efficiencies with a Scalable Parallel SNSPD
- How well can superconducting nanowire single-photon detectors resolve photon number?