Time-Efficient Object Recognition in Quantum Ghost Imaging

Chané Moodley*, Alice Ruget, Jonathan Leach, Andrew Forbes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
45 Downloads (Pure)


Acquiring information at the fastest possible rate is often desirable, particularly in quantum ghost imaging which suffers from slow reconstruction speeds. Many computationally intense deep-learning methods have been implemented in an effort to speed up image acquisition times by retrieving image information. Often over-looked, machine learning methods can offer the same, if not better, speed up in image acquisition time by an object recognition process. Four machine learning algorithms are implemented and trained on a uniquely generated, noised, and blurred dataset of numerical digits 1 through 9. Of the tested recognition algorithms, logistic regression shows a 10× speed up in image acquisition time with a 99% prediction accuracy. Additionally, this reduction in acquisition time is achieved without any image denoising or enhancement prior to recognition, thereby reducing training and implementation time, as well as the computational intensity of the approach. This method can be implemented in real-time, requiring only 1/10th of the measurements needed for a general solution, making it ideal for quantum imaging and recognition of light sensitive structure.

Original languageEnglish
Article number2200109
JournalAdvanced Quantum Technologies
Issue number2
Early online date25 Dec 2022
Publication statusPublished - Feb 2023


  • ghost imaging
  • machine learning
  • quantum ghost imaging

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Electronic, Optical and Magnetic Materials
  • Nuclear and High Energy Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering


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