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.

翻译：本研究旨在向更广泛的受众介绍FRQI Pairs方法——一种利用量子循环神经网络与量子图像柔性表示进行图像分类的新颖方法。研究重点展示了一种将量子编码数据用于图像分类任务的创新途径，表明此类基于量子的方法可显著降低量子算法的复杂度。通过将FRQI Pairs方法与现有技术进行对比，凸显了将量子计算原理与神经网络架构相融合以发展量子机器学习的前景。