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.
@article{arxiv.2512.11499,
title = {FRQI Pairs method for image classification using Quantum Recurrent Neural Network},
author = {Rafał Potempa and Michał Kordasz and Sundas Naqeeb Khan and Krzysztof Werner and Kamil Wereszczyński and Krzysztof Simiński and Krzysztof A. Cyran},
journal= {arXiv preprint arXiv:2512.11499},
year = {2026}
}
Comments
This is the accepted version of a paper published in the Proceedings of the 2025 11th International Conference on Control, Decision and Information Technologies (CoDIT). The final published version is available at IEEE Xplore