由神经网络状态引导的二维量子自旋眼镜的零温度蒙特卡洛模拟
1INFN-Sezione di Perugia, Università di Camerino, School of Science and Technology, Physics Division, 62032 Camerino, Italy and , 06123 Perugia, Italy.
Physical review. E
|February 7, 2025
概括
这项研究使用量子蒙特卡洛算法与神经网络来模拟量子自旋玻璃模型. 结果显示,该方法有效地抑制了偏差,并揭示了旋转玻璃过渡的关键性质.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 量子力学就是量子力学.
- 计算物理 计算物理
背景情况:
- 量子自旋玻璃是复杂的磁性材料,表现出无序和合作现象.
- 由于量子波动和混乱,模拟这些系统在计算上具有挑战性.
研究的目的:
- 使用连续时间投影量子蒙特卡洛算法模拟2D量子旋转玻璃模型的基本状态.
- 研究神经网络引导波函数在减少模拟偏差方面的有效性.
- 确定关键指数并分析旋转重叠分布.
主要方法:
- 使用连续时间投影量子蒙特卡洛算法.
- 采用自学神经网络作为引导波函数来抑制人口控制偏见.
- 设计了一个两倍复制的哈密尔顿数来确定旋转重叠.
- 进行有限尺寸缩放分析以确定关键参数和指数.
主要成果:
- 神经网络指导波函数有效地抑制了人口控制偏差,直至100次旋转.
- 有限尺寸缩放分析给出了关键横向场和关键指数的估计.
- 旋转玻璃易感度和相关长度指数与文献值一致.
- 在旋转玻璃阶段的旋转重叠分布呈现出双峰形状,在零重叠时具有显著的重量.
结论:
- 开发的量子蒙特卡洛方法与神经网络指导是模拟量子自旋眼镜的可行方法.
- 这项研究为2D爱德华兹-安德森模型的关键行为和相位结构提供了宝贵的见解.
- 观察到的旋转重叠分布与旋转玻璃相的理论预测一致.
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