mlsanityData to 14 Oct 2026

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

arXiv 2504.19053 ↗First posted 26 Apr 2025quant-ph cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 26 Apr 2025.

Builds on

25 of 33 references.

References · 25

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  3. 2018On the Spectral Bias of Neural Networkscited by 1,341
  4. 2020Convolutional Occupancy Networkscited by 761
  5. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  6. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  7. 2023WIRE: Wavelet Implicit Neural Representationscited by 220
  8. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
  9. 2023FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functionscited by 78
  10. 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representationcited by 71
  11. 2021Seeing Implicit Neural Representations as Fourier Seriescited by 50
  12. 2021An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representationcited by 48
13 more
  1. 2021IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representationcited by 42
  2. 2024Improved Implicit Neural Representation with Fourier Reparameterized Trainingcited by 36
  3. 2022DINER: Disorder-Invariant Implicit Neural Representationcited by 30
  4. 2023Let Quantum Neural Networks Choose Their Own Frequenciescited by 22
  5. 2023I-MedSAM: Implicit Medical Image Segmentation with Segment Anythingcited by 22
  6. 2024Quantum Implicit Neural Representationscited by 16
  7. 2022HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworkscited by 12
  8. 2024'Eliv'agar: Efficient Quantum Circuit Search for Classificationcited by 12
  9. 2023Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responsescited by 8
  10. 2024Neural Radiance Fields in Medical Imaging: A Surveycited by 7
  11. 2025Benchmarking MedMNIST dataset on real quantum hardwarecited by 6
  12. 2024ReLUs Are Sufficient for Learning Implicit Neural Representationscited by 5
  13. 2024Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometrycited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesiscited by 1