Q-RUN: Quantum-Inspired Data Re-uploading Networks
Published:Dec 25, 2025 05:00
•1 min read
•ArXiv ML
Analysis
This paper introduces Q-RUN, a novel classical neural network architecture inspired by data re-uploading quantum circuits (DRQC). It addresses the scalability limitations of quantum hardware by translating the mathematical principles of DRQC into a classical model. The key advantage of Q-RUN is its ability to retain the Fourier-expressive power of quantum models without requiring quantum hardware. Experimental results demonstrate significant performance improvements in data and predictive modeling tasks, with reduced model parameters and decreased error compared to traditional neural network layers. Q-RUN's drop-in replacement capability for fully connected layers makes it a versatile tool for enhancing various neural architectures, showcasing the potential of quantum machine learning principles in guiding the design of more expressive AI.
Key Takeaways
- •Q-RUN is a classical neural network inspired by quantum data re-uploading circuits.
- •It overcomes the scalability limitations of quantum hardware while retaining Fourier-expressive power.
- •Q-RUN demonstrates superior performance in data and predictive modeling tasks compared to traditional methods.
Reference
“Q-RUN reduces model parameters while decreasing error by approximately one to three orders of magnitude on certain tasks.”