English(EN) Leveraging Quantum Measurement Randomness for Shot-Efficient Diffusion Models

量子测量噪声提升扩散模型效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种新颖的方法，通过利用量子测量的固有随机性来提高扩散模型的效率。该方法将量子读出过程中通常产生的噪声重新用作一种资源，以替代扩散采样器在生成过程中有意添加的部分噪声。通过在概率单纯形内构建扩散过程并采用补偿噪声，该方法可以用更少的采样次数匹配所需的输出特征。 AI

影响这项研究可能通过利用量子现象，导致更高效的生成模型，从而降低计算成本。

排序理由这是一篇研究论文，详细介绍了一种使用量子计算原理改进扩散模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Tianhao Guo, Dianxin Zheng, Yuhua Qian, Yaohui Zheng, Yuzhi Yang, Merouane Debbah, Chau Yuen · 2026-11-02 05:00

Leveraging Quantum Measurement Randomness for Shot-Efficient Diffusion Models

arXiv:2610.41721v1 Announce Type: cross Abstract: In quantum computing, the readout always carries finite-shot sampling noise. Suppressing this noise through repeated measurements raises the cost of a quantum neural network (QNN). Meanwhile, a stochastic diffusion sampler deliber…