Implicit neural representations have shown potential in various applications. However, accurately reconstructing the image or providing clear details via image super-resolution remains challenging. This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations. The frequency spectrum is well balanced by penalizing the low-frequency components, leading to the improved expressivity of quantum circuits. The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models. Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.
@article{arxiv.2504.19053,
title = {QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations},
author = {Hongni Jin and Gurinder Singh and Kenneth M. Merz},
journal= {arXiv preprint arXiv:2504.19053},
year = {2025}
}