Accelerating quantum optics experiments with statistical learning

Abstract

Quantum optics experiments, involving the measurement of low-probability photon events, are known to be extremely time-consuming. We present a new methodology for accelerating such experiments using physically-motivated ansatzes together with simple statistical learning techniques such as Bayesian maximum a posteriori estimation based on few-shot data. We show that it is possible to reconstruct time-dependent data using a small number of detected photons, allowing for fast estimates in under a minute and providing a one-to-two order of magnitude speed up in data acquisition time. We test our approach using real experimental data to retrieve the second order intensity correlation function, $G^{(2)}(\tau )$, as a function of time delay $\tau$ between detector counts, for thermal light as well as anti-bunched light emitted by a quantum dot driven by periodic laser pulses. The proposed methodology has a wide range of applicability and has the potential to impact the scientific discovery process across a multitude of domains.

arXiv comment5 pages, 3 figures

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• Accelerating ultrafast spectroscopy with compressive sensing
• High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection
• Optimized higher-order photon state classification by machine learning
• Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality