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Quantum Machine Learning

FRQI Pairs method for image classification using Quantum Recurrent Neural Network

arXiv
Authors: Rafał Potempa, Michał Kordasz, Sundas Naqeeb Khan, Krzysztof Werner, Kamil Wereszczyński, Krzysztof Simiński, Krzysztof A. Cyran

Year

2025

Paper ID

36637

Status

Preprint

Abstract Read

~2 min

Abstract Words

89

Citations

N/A

Abstract

This study aims to introduce the FRQI Pairs method to a wider audience, a novel approach to image classification using Quantum Recurrent Neural Networks (QRNN) with Flexible Representation for Quantum Images (FRQI). The study highlights an innovative approach to use quantum encoded data for an image classification task, suggesting that such quantum-based approaches could significantly reduce the complexity of quantum algorithms. Comparison of the FRQI Pairs method with contemporary techniques underscores the promise of integrating quantum computing principles with neural network architectures for the development of quantum machine learning.

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  • This study aims to introduce the FRQI Pairs method to a wider audience, a novel approach to image classification using Quantum Recurrent Neural Networks (QRNN) with Flexible...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.11499 [2] arXiv https://arxiv.org/abs/2512.11499 [3] Publisher https://arxiv.org/abs/2512.11499

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