Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify topological phases and transitions out of them. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.

中文翻译：

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使用预测模型无监督地识别拓扑相变

机器学习驱动的模型已被证明是识别物质相的强大工具。特别是，无监督方法有望帮助发现物质的新阶段，而无需任何先验理论知识。虽然对于以破坏对称性为特征的相位，使用无监督方法已被证明是成功的，但没有局部顺序参数的拓扑相位在没有监督的情况下似乎更难识别。在这里，我们使用一种无​​监督的方法来识别拓扑相和从中的转换。我们训练人工神经网络将配置数据或测量结果与哈密顿量中的温度或调谐参数等量相关联。然后，这些预测模型的准确性可以作为相变的指标。