Accelerating quantum optics experiments with statistical learning

TL;DR

This paper introduces a statistical learning approach to significantly speed up quantum optics experiments by reconstructing photon correlation data with minimal measurements, enabling rapid data acquisition and analysis.

Contribution

It presents a novel methodology combining physically-motivated ansatzes with Bayesian estimation to accelerate quantum optics experiments using few-shot data.

Findings

Achieved 10-100x faster data acquisition in quantum optics experiments.

Successfully reconstructed $G^{(2)}( au)$ with minimal photon detections.

Demonstrated applicability to thermal and quantum dot light sources.

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.