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English(EN) QuanVI: Score-based Variational Inference via Quantum Maximally Mixed States

新的QuanVI算法使用受量子启发的算法进行高维推断

研究人员推出了一种新颖的评分式变分推断算法QuanVI,该算法解决了高维应用中的挑战。通过将混合态密度算符表述与使用矩阵乘积算符(MPO)结构的量子张量网络(QTN)参数化相结合,QuanVI可以更有效地表示退化的低能子空间。这种方法压缩了密度算符以防止参数呈指数增长,并在低维度上与精确解达成一致,同时扩展到高维基准测试。 AI

影响 引入了一种新颖的变分推断方法,有望提高复杂模型中贝叶斯近似的可扩展性和准确性。

排序理由 该集群描述了在arXiv上的研究论文中提出的一种新算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

新的QuanVI算法使用受量子启发的算法进行高维推断

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该集群描述了在arXiv上的研究论文中提出的一种新算法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuchen Cong, Zerui Tao, Chao Li, Zhe Sun, Qibin Zhao ·

    QuanVI:基于量子最大混合态的变分推断

    arXiv:2609.39164v1 Announce Type: new Abstract: Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variational distribution and the target. A prior score-VI approach formulates this opt…