A brain-inspired paradigm for scalable quantum vision
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916937951346688 |
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| author | Duan, Chenghua Li, Xiuxing Zhao, Wending Yao, Lin Li, Qing Li, Ziyu Li, Fukang Ma, Junhao Wu, Xia |
| author_facet | Duan, Chenghua Li, Xiuxing Zhao, Wending Yao, Lin Li, Qing Li, Ziyu Li, Fukang Ma, Junhao Wu, Xia |
| contents | One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A brain-inspired paradigm for scalable quantum vision Duan, Chenghua Li, Xiuxing Zhao, Wending Yao, Lin Li, Qing Li, Ziyu Li, Fukang Ma, Junhao Wu, Xia Quantum Physics One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems. |
| title | A brain-inspired paradigm for scalable quantum vision |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2509.05919 |