Ian Walmsley

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.

Abstract:

A hybrid interface of solid-state single-photon sources and atomic quantum memories is a long sought-after goal in photonic quantum technologies. Here, we demonstrate deterministic storage and retrieval of light from a semiconductor quantum dot in an atomic ensemble quantum memory at telecommunications wavelengths. We store single photons from an indium arsenide quantum dot in a high-bandwidth rubidium vapor-based quantum memory, with a total internal memory efficiency of (12.9 ± 0.4)%. The signal-to-noise ratio of the retrieved light field is 18.2 ± 0.6, limited only by detector dark counts.