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English(EN) Noise-regularized training of quantum circuit Born machines under a finite shot budget

量子玻色子机：噪声缓解与经典训练方法探索

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了量子电路玻色子机（QCBM）的训练，这是一种量子生成模型。第一篇论文介绍了GENIE框架，该框架旨在管理QCBM训练过程中的硬件噪声，并证明其在各种数据类型上均优于固定噪声水平。第二篇论文详细介绍了一种经典训练条件IQ玻色子机的方法，证明了它们能够捕获复杂的条件分布，并在Curie-Weiss自旋模型等模型上优于对角线条件。 AI

影响 量子生成模型的进步可能为复杂数据分析和模拟带来新方法。

排序理由 两篇arXiv论文详细介绍了训练量子玻色子机的新颖方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

量子玻色子机：噪声缓解与经典训练方法探索

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两篇arXiv论文详细介绍了训练量子玻色子机的新颖方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hoang M. Ngo, Quan Nguyen, Jung Taek Seo, Wanli Xing, My T. Thai ·

    有限采样预算下量子电路玻尔兹曼机的噪声正则化训练

    arXiv:2611.06630v1 Announce Type: cross Abstract: Quantum circuit Born machines (QCBMs) can learn distributions that are hard to sample from classically, but their value depends on generalizing from finite training data. Here, we study how hardware noise affects this generalizati…

  2. arXiv cs.LG TIER_1 English(EN) · Oriol Ball\'o-Gimbernat, Emiliano Tolotti, Laia Domingo, Fernando Vilari\~no, Adan Garriga ·

    条件瞬时量子多项式时间玻色机经典训练

    arXiv:2611.05483v1 Announce Type: cross Abstract: Instantaneous quantum polynomial-time (IQP) Born machines are attractive quantum generative models, as certain IQP circuit families are conjectured to be hard to sample, yet they can be trained classically. Previously proposed con…

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