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arXiv · 2509.05919

A brain-inspired paradigm for scalable quantum vision

Chenghua Duan·Xiuxing Li·Wending Zhao·Lin Yao·Qing Li·Ziyu Li·Fukang Li·Junhao Ma·Xia Wu
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Abstract

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.

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Chenghua Duan, Xiuxing Li, Wending Zhao, Lin Yao, Qing Li, Ziyu Li, Fukang Li, Junhao Ma, Xia Wu. 2025-09-07. A brain-inspired paradigm for scalable quantum vision. https://arxiv.org/abs/2509.05919

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