论文标题
边际量子神经网络模型的量子相检测概括
Quantum phase detection generalisation from marginal quantum neural network models
论文作者
论文摘要
量子机学习在提取有关量子状态的信息时提供了有希望的优势，例如相图。但是，使用培训标签是任何有监督方法的主要瓶颈，阻止了对新物理学的见解。在这封信中，使用量子卷积神经网络，我们通过仅在相图的边缘点上训练分析解决方案的模型的相位图来克服该限制，该模型缺少分析解决方案。更具体地说，我们认为具有铁磁，顺磁性和反相的轴向下一个最邻居（Annni）Hamiltonian，表明可以再现整个相图。
Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising (ANNNI) Hamiltonian, which possesses a ferromagnetic, paramagnetic and antiphase, showing that the whole phase diagram can be reproduced.
