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Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform.

Hossein Najafzadeh1, Zahra Raissi2,3, Shole Golmohammady4

  • 1Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.

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|November 22, 2025
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Summary

Deep learning models accurately classify quantum emission signals from WS₂ nanobubbles, achieving up to 99.4% accuracy. This method enhances quantum materials characterization and spectral distinguishability for quantum technologies.

Keywords:
Deep learningQuantum emissionQuantum sensingTransfer learningWS₂ monolayer

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Area of Science:

  • Quantum Materials Science
  • Machine Learning Applications
  • Spectroscopy

Background:

  • Characterizing quantum emission signals from materials like WS₂ monolayer nanobubbles is crucial but challenging.
  • Assessing spectral distinguishability is key for quantum information applications.

Purpose of the Study:

  • To develop and evaluate deep learning models for classifying quantum emission signals from WS₂ monolayer nanobubbles.
  • To assess the performance of different convolutional neural network architectures for this classification task.

Main Methods:

  • Quantum emission signals were preprocessed and transformed into RGB images using Continuous Wavelet Transform (CWT).
  • Three CNN architectures (ResNet50, VGG16, Xception) were trained and evaluated using fivefold cross-validation.
  • Classification accuracy was assessed across different spectral band combinations.

Main Results:

  • All evaluated models achieved high classification accuracy, with VGG16 reaching 99.4% mean accuracy.
  • Perfect accuracy was observed for spectrally distant bands, while adjacent bands presented a greater challenge (96.5% for VGG16).
  • Xception demonstrated high computational efficiency, converging in as few as 2 epochs.

Conclusions:

  • Deep learning combined with CWT offers a robust framework for quantum emission signal classification.
  • This approach has significant implications for quantum photonics, cryptography, and sensing.
  • The study addresses data scarcity in quantum systems via transfer learning, paving the way for future quantum technology development.