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FRQI Pairs method for image classification using Quantum Recurrent Neural Network

arXiv 2512.11499 ↗First posted 12 Dec 2025quant-ph cs.LGcited by 0

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Abstract (arXiv)

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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References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020Layerwise learning for quantum neural networkscited by 88
  3. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 85
  4. 2020Recurrent Quantum Neural Networkscited by 80
  5. 2017Quantum Neuron: an elementary building block for machine learning on quantum computerscited by 35
  6. 2022Rapid training of quantum recurrent neural networkscited by 13
  7. 2016Pattern recognition on the quantum Bloch spherecited by 6
  8. 2021Comparing concepts of quantum and classical neural network models for image classification taskcited by 5
  9. 2022Random Quantum Neural Networks (RQNN) for Noisy Image Recognitioncited by 4
  10. 2015Quantum image classification using principal component analysiscited by 1

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