论文标题
使用复发性神经网络对量子自旋模型的迭代重新培训
Iterative Retraining of Quantum Spin Models Using Recurrent Neural Networks
论文作者
论文摘要
由于Hilbert尺寸的指数缩放，建模量子多体系统非常具有挑战性。找到波力的有效压缩是建立可扩展模型的关键。在这里，我们介绍了迭代重新培训，这是一种模拟使用复发神经网络（RNN）的批量量子系统的方法。通过将晶格向量中的翻译映射到RNN的时间索引，我们能够有效地捕获大晶格的近乎翻译不变性。我们表明，我们可以使用此对称映射来模拟一个和二维的非常大的系统。我们这样做是通过“成长”模型，迭代地在逐渐更大的晶格上重新训练相同的模型，直到边缘效应可忽略不计。我们认为，该方案比密度基质重质化组更自然地概括到更高的尺寸。
Modeling quantum many-body systems is enormously challenging due to the exponential scaling of Hilbert dimension with system size. Finding efficient compressions of the wavefunction is key to building scalable models. Here, we introduce iterative retraining, an approach for simulating bulk quantum systems that uses recurrent neural networks (RNNs). By mapping translations in the lattice vector to the time index of an RNN, we are able to efficiently capture the near translational invariance of large lattices. We show that we can use this symmetry mapping to simulate very large systems in one and two dimensions. We do so by 'growing' our model, iteratively retraining the same model on progressively larger lattices until edge effects become negligible. We argue that this scheme generalizes more naturally to higher dimensions than Density Matrix Renormalization Group.
