Summary of Quantum Implicit Neural Representations, by Jiaming Zhao et al.
Quantum Implicit Neural Representations
by Jiaming Zhao, Wenbo Qiao, Peng Zhang, Hui Gao
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to implicit neural representation, called Quantum Implicit Representation Network (QIREN), is proposed in this paper. QIREN is a quantum generalization of Fourier Neural Networks (FNNs) that utilizes neural networks to parameterize the implicit function of signals such as images and sounds. The key innovation is that QIREN leverages quantum computing principles to model high-frequency components more accurately than traditional FNNs, which struggle with these components. Theoretical analysis shows that QIREN has a quantum advantage over classical FNNs. Experimental results demonstrate QIREN’s superior performance in signal representation, image superresolution, and image generation tasks compared to state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to represent signals like images and sounds using neural networks. The method, called Quantum Implicit Representation Network (QIREN), is an improvement over existing methods that struggle with high-frequency components of signals. QIREN uses special quantum computing principles to do better modeling of these high-frequency parts. This leads to better results in tasks like image superresolution and image generation. |
Keywords
» Artificial intelligence » Generalization » Image generation
