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English(EN) Representational separation between unitary and channel quantum generative models via shared classical randomness at shallow depth

量子生成模型通过经典随机性获得表示能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员已经证明，将共享经典随机性纳入量子生成模型可以使它们比纯粹的单一模型代表更广泛的分布，即使在浅层电路深度下也是如此。这一发现解决了关于这种随机性是否为大型系统提供可证明分离的长期问题。研究表明，通过将由单个随机比特控制的局部 Pauli 运算添加到浅层单一电路中，通道模型可以生成浅层单一模型无法通过有界连接重现的远程相关性。 AI

影响 增进对量子生成模型的理解，可能影响未来量子计算领域的人工智能研究。

排序理由 详细介绍量子生成模型理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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

量子生成模型通过经典随机性获得表示能力

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详细介绍量子生成模型理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Arunava Majumder, Marius Krumm, Hendrik Poulsen Nautrup, Hans J. Briegel ·

    通过浅层共享经典随机性实现单元和通道量子生成模型之间的表征分离

    arXiv:2608.05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to shallow unitary Born models. Introducing stochast…

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