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.
View on arXiv@article{jin2025_2504.19053,
title={ QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations },
author={ Hongni Jin and Gurinder Singh and Kenneth M. Merz Jr },
journal={arXiv preprint arXiv:2504.19053},
year={ 2025 }
}