论文标题
通过高斯拟合数概率分布检测相图检测
Phase Diagram Detection via Gaussian Fitting of Number Probability Distribution
论文作者
论文摘要
我们研究了具有全球保守数量粒子的量子多体系统的子部分的数量概率密度函数。我们提出了一个线性拟合协议，能够绘制出丰富的一维扩展Bose-Hubbard模型的地面相图：与更复杂的传统和机器学习技术相比，结果是可比较的。我们认为，研究的数量应被视为最有用的两分性能之一，并且在原子气体实验中很容易访问。
We investigate the number probability density function that characterizes sub-portions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.
