.png){kind=icon}
.png){kind=icon}
Return
Accelerating quantum optics experiments with statistical learning
.png){kind=icon}
OA
AI
DOI：10.1063/1.5143786.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Quantum optics experiments, involving the measurement of low-probability photon events, are known to be extremely time-consuming. We present a 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.
Keywords:
INTENSITY INTERFEROMETRY
IMAGE
SUPERRESOLUTION
RECONSTRUCTION
ENHANCEMENT
GENERATION
BLINKING
PHOTONS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
10.4W
Citations:
17.8W