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Quantum Machine Learning

Analysis of five techniques for the internal representation of a digital image inside a quantum processor

arXiv
Authors: Sundaraja Sitharama Iyengar, Latesh K. J. Kumar, Mario Mastriani

Year

2020

Paper ID

21744

Status

Preprint

Abstract Read

~2 min

Abstract Words

87

Citations

N/A

Abstract

In this paper, five techniques, for the representation of a digital image inside a quantum processor, are compared. The techniques are: flexible representation of quantum images (FRQI), novel enhanced quantum representation (NEQR), generalized quantum image representation (GQIR), multi-channel representation for quantum images (MCQI), and quantum Boolean image processing (QBIP). The comparison will be based on implementations on the Quirk simulator, and on the IBM Q Experience processors, from the point of view of performance, robustness (noise immunity), deterioration of the outcomes due to decoherence, and technical viability.

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.
  • In this paper, five techniques, for the representation of a digital image inside a quantum processor, are compared.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2008.01081 [2] arXiv https://arxiv.org/abs/2008.01081 [3] Publisher https://arxiv.org/abs/2008.01081

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