FRQI Pairs method for image classification using Quantum Recurrent Neural Network
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918300928180224 |
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| author | Potempa, Rafał Kordasz, Michał Khan, Sundas Naqeeb Werner, Krzysztof Wereszczyński, Kamil Simiński, Krzysztof Cyran, Krzysztof A. |
| author_facet | Potempa, Rafał Kordasz, Michał Khan, Sundas Naqeeb Werner, Krzysztof Wereszczyński, Kamil Simiński, Krzysztof Cyran, Krzysztof A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FRQI Pairs method for image classification using Quantum Recurrent Neural Network Potempa, Rafał Kordasz, Michał Khan, Sundas Naqeeb Werner, Krzysztof Wereszczyński, Kamil Simiński, Krzysztof Cyran, Krzysztof A. Quantum Physics Machine Learning 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. |
| title | FRQI Pairs method for image classification using Quantum Recurrent Neural Network |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2512.11499 |