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Quantum Machine Learning

QHSL: a quantum hue, saturation, and lightness color model

arXiv
Authors: Fei Yan, Nianqiao Li, Kaoru Hirota

Year

2020

Paper ID

22130

Status

Preprint

Abstract Read

~2 min

Abstract Words

122

Citations

N/A

Abstract

Quantum image processing employs quantum computing to capture, manipulate, and recover images in various formats. This requires representations of encoded images using the quantum mechanical composition of any potential computing hardware. In this study, a quantum hue, saturation, and lightness (QHSL) color model is proposed to organize and conceptualize color-assignment attributes using the properties of quantum mechanics (i.e., entanglement and parallelism). The proposed color model is used to define representations of a two-dimensional QHSL image and investigate its data storage, chromatic transformation, and pseudocolor applications. The QHSL representation is introduced for the production of quantum images using triple perceptually relevant components. The efficient use of QHSL images is further explored for applications in the fields of computer vision and image analysis.

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.
  • Quantum image processing employs quantum computing to capture, manipulate, and recover images in various formats.

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