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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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