Identifying phases of matter presents considerable challenges, particularly within the domain of quantum theory, where the complexity of ground states appears to increase exponentially with system size. Quantum many-body systems exhibit an array of complex entanglement structures spanning distinct phases. Although extensive research has explored the relationship between quantum phase transitions and quantum entanglement, establishing a direct, pragmatic connection between them remains a critical challenge. In this work, we present a novel and efficient quantum phase transition classifier, utilizing disentanglement with reinforcement learning-optimized variational quantum circuits. We demonstrate the effectiveness of this method on quantum phase transitions in the transverse field Ising model (TFIM) and the XXZ model. Moreover, we observe the algorithm's ability to learn the Kramers-Wannier duality pertaining to entanglement structures in the TFIM. Our approach not only identifies phase transitions based on the performance of the disentangling circuits but also exhibits impressive scalability, facilitating its application in larger and more complex quantum systems. This study sheds light on the characterization of quantum phases through the entanglement structures inherent in quantum many-body systems.

中文翻译：

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通过单量子位解缠学习量子相


识别物质的相提出了相当大的挑战，特别是在量子理论领域，基态的复杂性似乎随着系统尺寸呈指数级增加。量子多体系统表现出一系列跨越不同相的复杂纠缠结构。尽管广泛的研究已经探索了量子相变和量子纠缠之间的关系，但在它们之间建立直接、务实的联系仍然是一个严峻的挑战。在这项工作中，我们提出了一种新颖且高效的量子相变分类器，利用强化学习优化的变分量子电路的解缠结。我们证明了该方法在横向场伊辛模型 (TFIM) 和 XXZ 模型中的量子相变方面的有效性。此外，我们观察到该算法学习与 TFIM 中纠缠结构相关的 Kramers-Wannier 对偶性的能力。我们的方法不仅根据解缠结电路的性能识别相变，而且还表现出令人印象深刻的可扩展性，促进其在更大、更复杂的量子系统中的应用。这项研究通过量子多体系统固有的纠缠结构揭示了量子相的表征。