Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source Quality

TL;DR

This paper explores using data augmentation to quantify uncertainty in early estimates of single-photon source quality, highlighting the importance of accounting for stochastic variability to avoid overconfidence in experimental results.

Contribution

It introduces a data augmentation approach to quantify uncertainty in SPS quality metrics, emphasizing the need for cautious interpretation of early estimates.

Findings

Bootstrapped samples reveal significant uncertainty from Poisson variability.

Standard least-squares fitting performs comparably to Poisson likelihood fitting.

Reducing background counts improves fit accuracy but doesn't address Poisson variability.

Abstract

Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.