Neural Ordinary Differential Equations for Forecasting and Accelerating Photon Correlation Spectroscopy

Abstract

Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon correlation Fourier spectroscopy (PCFS) is one such technique that measures time-resolved single-emitter line shapes and offers additional spectral information over Hong–Ou–Mandel two-photon interference but requires long experimental acquisition times. Here, we demonstrate a neural ordinary differential equation model, g2NODE, that can forecast a complete and noise-free interferometry experiment from a small subset of noisy correlation functions. We demonstrate this for simulated and experimental data, where g2NODE utilizes 10–20 noisy measured photon correlation functions to create entire denoised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from hours to minutes. Our work presents a new deep learning approach to greatly accelerate the use of photon correlation spectroscopy as an experimental characterization tool for novel quantum emitter materials.