{"ID":2827379,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20654","arxiv_id":"2512.20654","title":"Q-RUN: Quantum-Inspired Data Re-uploading Networks","abstract":"Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks and have been shown to outperform classical neural networks in fitting high-frequency functions. However, their practical application is limited by the scalability of current quantum hardware. In this paper, we introduce the mathematical paradigm of DRQC into classical models by proposing a quantum-inspired data re-uploading network (Q-RUN), which retains the Fourier-expressive advantages of quantum models without any quantum hardware. Experimental results demonstrate that Q-RUN delivers superior performance across both data modeling and predictive modeling tasks. Compared to the fully connected layers and the state-of-the-art neural network layers, Q-RUN reduces model parameters while decreasing error by approximately one to three orders of magnitude on certain tasks. Notably, Q-RUN can serve as a drop-in replacement for standard fully connected layers, improving the performance of a wide range of neural architectures. This work illustrates how principles from quantum machine learning can guide the design of more expressive artificial intelligence.","short_abstract":"Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks and have been shown to outperform classical neural networks in fitting high-frequency functions. However, their practical application is limited by the scalability of current quantum hardware. In this paper, we introduc...","url_abs":"https://arxiv.org/abs/2512.20654","url_pdf":"https://arxiv.org/pdf/2512.20654v1","authors":"[\"Wenbo Qiao\",\"Shuaixian Wang\",\"Peng Zhang\",\"Yan Ming\",\"Jiaming Zhao\"]","published":"2025-12-18T04:12:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"quant-ph\"]","methods":"[]","has_code":false}