# FRQI Pairs method for image classification using Quantum Recurrent Neural Network

Source: [FRQI Pairs method for image classification using Quantum Recurrent Neural Network](<https://www.sota2.com/research/paper/693f95e96cfd488761a818af>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-27T05:00:04.746Z

Authors: Rafa{\\l} Potempa, Micha{\\l} Kordasz, Sundas Naqeeb Khan, Krzysztof Werner, Kamil Wereszczy\\'nski, Krzysztof Simi\\'nski, Krzysztof A. Cyran

Published: 2025-12-12T11:52:48.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.11499>)

[PDF](<https://arxiv.org/pdf/2512.11499.pdf>)