Q-RUN: Quantum-Inspired Data Re-uploading Networks

• URL: http://arxiv.org/abs/2512.20654v1
• Date: Thu, 18 Dec 2025 04:12:09 GMT
• Title: Q-RUN: Quantum-Inspired Data Re-uploading Networks
• Authors: Wenbo Qiao, Shuaixian Wang, Peng Zhang, Yan Ming, Jiaming Zhao,
• Abstract summary: Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks.<n>We introduce the mathematical paradigm of DRQC into classical models by proposing a quantum-inspired data re-uploading network (Q-RUN)<n>Q-RUN retains the Fourier-expressive advantages of quantum models without any quantum hardware.
• Score: 9.564540024568245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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