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arXiv 2305.03394cond-mat.stat-mechcond-mat.quant-gasquant-ph
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用于次近邻Ising模型的神经网络量子态的监督训练

Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model

Zheyu Wu, Remmy Zen, Heitor P. Casagrande, Stéphane Bressan, Dario Poletti

AI总结:

本文探索并比较了前馈神经网络量子态在次近邻Ising模型不同相态下的监督训练策略,发现通过对网络进行缩放,重叠损失函数能在所有相中实现更好的训练效果。

摘要:

不同的神经网络架构可以通过无监督或有监督的训练来表示量子态。我们探索并比较了前馈神经网络量子态监督训练的不同策略。我们实证且比较地评估了前馈神经网络量子态在不同物相中的性能,涵盖了架构变体、不同超参数以及两种不同的损失函数,分别称为均方误差和重叠度。我们考虑了次近邻Ising模型,因其相态具有多样性,并重点关注其顺磁相、铁磁相和配对反铁磁相。我们观察到,在对神经网络进行缩放的前提下,重叠损失函数能够在所有相中实现更好的模型训练。

英文摘要:

Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised training of feed forward neural network quantum states. We empirically and comparatively evaluate the performance of feed forward neural network quantum states in different phases of matter for variants of the architecture, for different hyper-parameters, and for two different loss functions, to which we refer as \emph{mean-squared error} and \emph{overlap}, respectively. We consider the next-nearest neighbor Ising model for the diversity of its phases and focus on its paramagnetic, ferromagnetic, and pair-antiferromagnetic phases. We observe that the overlap loss function allows better training of the model across all phases, provided a rescaling of the neural network.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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