QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations

• URL: http://arxiv.org/abs/2504.19053v1
• Date: Sat, 26 Apr 2025 23:40:33 GMT
• Title: QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations
• Authors: Hongni Jin, Gurinder Singh, Kenneth M. Merz Jr,
• Abstract summary: This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations.<n>The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models.<n>Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.
• Score: 1.874615333573157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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