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Quantum Machine Learning Quantum Simulation

Quantum Block-Matching Algorithm using Dissimilarity Measure

arXiv
Authors: M. Martínez-Felipe, J. Montiel-Pérez, V. Onofre, A. Maldonado-Romo, Ricky Young

Year

2023

Paper ID

54377

Status

Preprint

Abstract Read

~2 min

Abstract Words

98

Citations

N/A

Abstract

Finding groups of similar image blocks within an ample search area is often necessary in different applications, such as video compression, image clustering, vector quantization, and nonlocal noise reduction. A block-matching algorithm that uses a dissimilarity measure can be applied in such scenarios. In this work, a measure that utilizes the quantum Fourier transform or the Swap test based on the Euclidean distance is proposed. Experiments on small cases with ideal and noisy simulations are implemented. In the case of the Swap test, the IBM and IonQ quantum devices have been used, demonstrating potential for future near-term applications.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Finding groups of similar image blocks within an ample search area is often necessary in different applications, such as video compression, image clustering, vector...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2309.15792 [2] arXiv https://arxiv.org/abs/2309.15792 [3] Publisher https://arxiv.org/abs/2309.15792

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