平面二极旋转链的地面状态与经常性神经网络
Tobias Serwatka1, Pierre-Nicholas Roy1
1Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
The Journal of chemical physics
|June 10, 2024
概括
一种循环神经网络 (RNN) 方法准确地确定了二极旋转器的基本状态,成功地处理了带有和没有信号问题的系统,以揭示量子相位过渡.
科学领域:
- 量子物理学的量子物理学
- 计算凝聚物质物理学 计算凝聚物质物理学
- 机器学习在物理学中的应用
背景情况:
- 了解量子多体系统的基本状态属性至关重要.
- 通常采用变量方法,但可能面临诸如符号问题之类的挑战.
研究的目的:
- 应用一次性神经网络 (RNN) 变化替代器进行基态计算.
- 为了研究基础集对多体波函数的符号结构的影响.
- 在二极旋转器系统中识别量子相位过渡.
主要方法:
- 在变化优化框架内使用了循环神经网络 (RNN) 架构.
- 测试了各种本地基数组,以分析它们对地面状态波函数的影响.
- 运用量子化来计算纠性质.
主要成果:
- 作为RNN的替代品,RNN有效地处理了带有和没有标志问题的系统.
- 精确的相关函数和Binder参数被计算为链路,最多50个旋转器.
- 获得了精确的纠特性,使量子相变的识别成为可能.
结论:
- RNN变化方法是研究量子多体系统的强大工具.
- 该研究成功地确定了准电和铁电相之间的量子相变.
- 这种方法为探索复杂的量子现象提供了一条可行的途径.
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