发表机构
Perimeter Institute for Theoretical Physics; Harvard University; Ecole Polytechnique Federale de Lausanne; Princeton University; University of Waterloo（圆周理论物理研究所; 哈佛大学; 洛桑联邦理工学院; 普林斯顿大学; 滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个结合可微分编程与无监督学习的自主系统，沿自适应轨迹搜索哈密顿量空间，在无需相标签的情况下高效发现量子相，并在多个模型中验证其优于随机采样。
AI 中文摘要
长期以来，理解物质的量子相一直依赖于物理学家的直觉以及对称性和拓扑等数学工具。尽管这些方法非常成功，但它们并未提供一种通用的方式来探索一个其组织原理事先未知的哈密顿量空间。在这项工作中，我们引入了一个完全自主的系统，结合可微分编程和无监督学习用于量子相发现。该搜索沿着自适应轨迹评估基态数据，而不是在预定的参数网格上进行。我们使用三种不同的求解器演示了该系统，并在相同的基态评估预算下与随机采样进行了基准测试。在一个包含多达200个不同相的广义簇链上，该搜索在相同预算下发现了多达25个额外的相，并且在给予其三十倍预算的情况下与随机采样相匹配。在一个50参数的Chern绝缘体中，它达到了在此处考虑的简单谐振构造未获得的扇区，该族中的逆问题仍然开放，同时恢复了采样发现的所有扇区。我们的结果确立了自主的、梯度驱动的哈密顿量空间探索作为在没有相标签或指定目标相的情况下发现量子相的实用途径。
英文摘要
Understanding quantum phases of matter has long relied on physicists' intuition and mathematical tools such as symmetry and topology. Remarkably successful as these approaches have been, they provide no universal way to explore a Hamiltonian space whose organizing principle is not known in advance. In this work, we introduce a fully autonomous system combining differentiable programming and unsupervised learning for quantum phase discovery. The search evaluates ground-state data along an adaptive trajectory rather than on a predetermined parameter grid. We demonstrate the system with three different solvers and benchmark it against random sampling at equal ground-state-evaluation budgets. On a generalized cluster chain hosting up to $200$ distinct phases, the search finds up to $25$ more phases at the same budget, and matches random sampling given thirty times its budget. On a $50$-parameter Chern insulator, it reaches sectors not obtained by the simple harmonic constructions considered here, in a family whose inverse problem remains open, while recovering all sectors found by sampling. Our results establish autonomous, gradient-driven exploration of Hamiltonian space as a practical route to discovering quantum phases without phase labels or a prescribed target phase.
Comments10 pages, 5 figures, https://github.com/shiyu-zhou-7/diff_phase
