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Neural Ordinary Differential Equations for Forecasting and Accelerating Photon Correlation Spectroscopy.

Andrew H Proppe1, Kin Long Kelvin Lee1,2, Weiwei Sun1

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

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|January 6, 2025
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Summary
This summary is machine-generated.

We developed g2NODE, a deep learning model that significantly speeds up quantum optical property evaluation for single-photon emitters. This AI tool uses minimal data to generate complete, noise-free experiments, reducing acquisition time by up to 20x.

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Area of Science:

  • Quantum Optics
  • Materials Science
  • Artificial Intelligence

Background:

  • Evaluating quantum optical properties of solid-state single-photon emitters is crucial but time-consuming.
  • Photon correlation Fourier spectroscopy (PCFS) provides detailed spectral information but requires extensive experimental time.

Purpose of the Study:

  • To develop a novel deep learning model to accelerate the characterization of quantum emitters.
  • To reduce the experimental acquisition time for photon correlation spectroscopy.

Main Methods:

  • A neural ordinary differential equation model, named g2NODE, was developed.
  • g2NODE forecasts complete, noise-free interferometry experiments from a small subset of noisy correlation functions.
  • The model was validated using both simulated and experimental data.

Main Results:

  • g2NODE successfully generated entire denoised interferograms from 10-20 noisy measurements.
  • This approach enabled up to a 20-fold speedup in experimental acquisition time, reducing hours to minutes.
  • The model accurately forecasts experiments up to 200 stage positions.

Conclusions:

  • g2NODE offers a significant acceleration for photon correlation spectroscopy.
  • This deep learning approach enhances the utility of PCFS for characterizing novel quantum emitter materials.
  • The method presents a new AI-driven strategy for experimental characterization in quantum optics.