接口分支随机步行与大都市采样:辅助场中的约束释放量子蒙特卡洛
Zhi-Yu Xiao1, Hao Shi2, Shiwei Zhang3
1Department of Physics, College of William & Mary, Williamsburg, Virginia 23187, United States.
Journal of chemical theory and computation
|September 4, 2023
概括
这项研究引入了一种新的方法,将分支随机步行与马尔科夫链蒙特卡洛采样相结合,用于量子模拟. 这种方法提高了辅助场量子蒙特卡洛计算的准确性,为原子和分子提供了近乎精确的结果.
科学领域:
- 计算物理 计算物理
- 量子化学 是一个量子化学.
背景情况:
- 辅助场量子蒙特卡罗 (AFQMC) 是量子模拟的一个强大的方法.
- 在AFQMC中使用分支随机步行来通过约束来解决信号/阶段问题.
- 使用试验波函数的近似约束可以引入系统错误.
研究的目的:
- 开发一种将分支随机走路与马尔科夫链蒙特卡洛 (MCMC) 采样接口的方法.
- 为了在量子蒙特卡洛计算中实现这两种采样技术之间的无切换.
- 为了减少系统错误,提高AFQMC模拟的准确性.
主要方法:
- 接口分支随机步行与MCMC采样使用一般化的大都会算法.
- 采样通过分支随机步行生成的选定的虚拟时间路径.
- 将该方法应用于辅助场量子蒙特卡洛 (AFQMC) 计算.
主要成果:
- 证明了分支随机走路和MCMC采样的成功整合.
- 通过受约束路径采样实现了系统错误的显著减少.
- 获得了对原子和分子进行计算的近乎准确的结果.
- 开发了一种随机方法,用于在不改变计算缩放的情况下评估哈密尔顿式的方差.
结论:
- 拟议的接口方法提高了AFQMC的准确性和可靠性.
- 这种方法为计算相关函数和非通勤可观的函数提供了改进.
- 该技术广泛适用于AFQMC以外的各种蒙特卡洛模拟.
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