研究人员利用对数损失建立了在线量子态学习的遗憾下界。这项工作对量子态层析成像和矩阵均值置信序列的开发具有重要意义。该研究提出了一种样本最优的量子态层析成像算法，并提出了一种用于矩阵值随机变量均值的竞争性算法。 AI
排序理由 该集群包含一篇关于应用于量子态的机器学习理论研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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新研究详述量子态学习的遗憾下界
本文如何被排名
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- 该集群包含一篇关于应用于量子态的机器学习理论研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]
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Logarithmic Loss在线学习量子态的遗憾下界，及其在量子态层析和矩阵均值置信序列中的应用
arXiv:2610.21273v1 Announce Type: new Abstract: We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has applications to quantum state tomography, the task of estimating an unknown qua…