The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Methods of machine learning have been used in adjacent fields for effective feature extraction and dimensionality reduction of high-dimensional datasets. Recent studies have revealed that neural networks are further suitable for the determination of macroscopic phases of matter and associated phase transitions as well as efficient quantum state representation. In this work, we address quantum phase transitions in quantum spin chains, namely the transverse field Ising chain and the anisotropic XY chain, and show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases. Our neural network acts to predict the corresponding crossovers finite-size systems undergo. Our results extend to a wide class of interacting quantum many-body systems and illu...

中文翻译：

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用神经网络探测量子自旋链的临界度

量子系统的数值仿真通常需要指数级的自由度，这会转化为计算瓶颈。机器学习方法已在相邻领域中用于有效特征提取和高维数据集的降维。最近的研究表明，神经网络还适用于确定物质的宏观相和相关的相变以及有效的量子态表示。在这项工作中，我们解决了量子自旋链中的量子相变，即横向场伊辛链和各向异性XY链，并表明，即使没有隐藏层的神经网络也可以有效地训练以区分磁有序相和无序相。我们的神经网络可预测有限尺寸系统所经历的相应交叉。我们的结果扩展到相互作用的量子多体系统和照明系统的广泛类别。