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Quantum Machine Learning

Single-Pixel Pattern Recognition with Coherent Nonlinear Optics

arXiv
Authors: Ting Bu, Santosh Kumar, He Zhang, Irwin Huang, Yuping Huang

Year

2020

Paper ID

20193

Status

Preprint

Abstract Read

~2 min

Abstract Words

104

Citations

N/A

Abstract

We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network. It employs mode selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted nonlinear-optically. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the MNIST handwritten digit images, and up to 95.32% even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large size images, fast lidar data analyses, complex pattern recognition, and so on.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2010.02273 [2] arXiv https://arxiv.org/abs/2010.02273 [3] Publisher https://arxiv.org/abs/2010.02273

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