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Accelerating quantum optics experiments with statistical learning
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DOI：10.1063/1.5143786.png)
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摘要
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.
Keyword:
INTENSITY INTERFEROMETRY
IMAGE
SUPERRESOLUTION
RECONSTRUCTION
ENHANCEMENT
GENERATION
BLINKING
PHOTONS
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