


ICML 2024 | Signal representation is exponentially stronger, memory saving exceeds 35%, quantum implicit representation network is coming

The AIxiv column is a column where academic and technical content is published on this site. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com
Paper title: Quantum Implicit Neural Representations Paper authors: Jiaming Zhao, Wenbo Qiao, Peng Zhang*, Hui Gao Paper link: https://arxiv.org/abs /2406.03873

This work not only integrates quantum advantages into implicit neural representation, but also opens up a promising application direction for quantum neural networks - implicit neural representation. It is worth emphasizing that implicit neural representations have many other potential applications, such as representing scenes or 3D objects, time series prediction, and solving differential equations. For a large class of tasks that model continuous signals, we can consider introducing implicit representation networks as a basic component. Based on the theoretical and experimental foundations of this paper, we can extend QIREN to these applications in future work, and QIREN is expected to produce better results with fewer parameters in these fields. At the same time, we found a suitable application scenario for quantum machine learning. Thereby promoting further practical and innovative research within the quantum machine learning community.
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