Generation via Classical Noise Reuploading

Xin Wang; Rebing Wu

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arXiv · 2605.02343

Generation via Classical Noise Reuploading

Xin Wang·Rebing Wu

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Abstract

We propose a novel quantum generative model paradigm that fundamentally avoids the issue of extremely small post-selection probabilities present in previous models. Unlike existing methods that require multi-step noise addition and denoising, this paradigm enables direct single-step generation of quantum data, significantly improving generation efficiency while substantially reducing the complexity of training and quantum state preparation. Furthermore, by directly sampling classical noise to generate quantum states, the sampling process becomes easier to implement. Experimental results demonstrate that this paradigm outperforms existing quantum generative models in terms of generation quality.

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Xin Wang, Rebing Wu. 2026-05-04. Generation via Classical Noise Reuploading. https://arxiv.org/abs/2605.02343

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