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Updated: Jul 4, 2025

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将学习从赫密斯转移到非赫密斯的量子多体物理学
Sharareh Sayyad1, Jose L Lado2
1Max Planck Institute for the Science of Light, Staudtstraße 2, 91058 Erlangen, Germany.
概括
在赫密特系统上训练的机器学习模型可以在非赫密特模型中识别相位边界. 这种转移学习方法有效地揭示了无需重新培训的非赫米特阶段图.
科学领域:
- 量子多体物理学 量子多体物理学
- 机器学习应用程序 机器学习应用程序
- 凝聚物质理论 凝聚物质理论
背景情况:
- 识别相位边界对于理解量子多体模型至关重要.
- 在赫密斯系统方面取得了进展,但非赫密斯模型面临着数值和分析方面的挑战.
- 机器学习提供了一种新的方法,可以从可观测物中预测相位边界.
研究的目的:
- 调查在赫米特系统上训练的机器学习模型是否可以在非赫米特模型中识别相位边界.
- 为了证明转移学习在非赫米特物理学中的有效性.
- 建立一种用于分析非赫尔密斯现象的多功能策略.
主要方法:
- 使用机器学习方法.
- 仅仅在赫米特相关函数上训练模型.
- 将训练模型应用于非赫米特交互模型.
主要成果:
- 机器学习方法成功地确定了非赫米特交互模型的相位边界.
- 经过赫米蒂安训练的机器学习算法被重新部署到非赫米蒂安模型中,而不需要重新训练.
- 使用这种转移学习方法揭示了非赫米特阶段图.
结论:
- 赫米斯式机器学习算法可以有效地应用于非赫米斯式模型.
- 转移学习是一种多功能策略,用于在机器学习中利用赫米斯物理学来学习非赫米斯现象.
- 这种方法克服了分析非赫米特式多体系统的局限性.
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