用神经微分方程从少量噪声数据预测量子发射器光谱，提速20倍。
Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
- 将测量数据编码为初值问题，通过神经微分方程外推完整干涉图。
- 仅用10组噪声数据即可重建200个延迟位置的去噪干涉图。
- 适合需要快速表征量子发射材料的研究者，尤其适用于高通量筛选。
深度神经网络可从数据中学习复杂动态并重建稀疏或噪声信号，从而加速和增强实验测量。评估固态单光子发射器的量子光学特性通常耗时，需进行干涉型光子相关实验，如光子相关傅里叶光谱（PCFS），用于测量时间分辨的单发射体线型。本文展示了一种潜在神经常微分方程模型，可从少量噪声相关函数中预测完整的无噪声PCFS实验结果。通过将测得的光子相关性编码为初值问题，该模型可推进至任意数量的干涉仪延迟位置。我们使用10组噪声光子相关函数，外推出最多200个级联位置的去噪干涉图，使实验采集时间从约3小时缩短至10分钟，实现高达20倍的速度提升。本工作提出一种新方法，显著加速基于深度学习的新型量子发射材料的实验表征。
原文摘要 · Abstract (English)
Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments, such as Photon correlation Fourier spectroscopy (PCFS) which measures time-resolved single emitter lineshapes. Here, we demonstrate a latent neural ordinary differential equation model that can forecast a complete and noise-free PCFS experiment from a small subset of noisy correlation functions. By encoding measured photon correlations into an initial value problem, the NODE can be propagated to an arbitrary number of interferometer delay times. We demonstrate this with 10 noisy photon correlation functions that are used to extrapolate an entire de-noised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from $\sim$3 hours to 10 minutes. Our work presents a new approach to greatly accelerate the experimental characterization of novel quantum emitter materials using deep learning.
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