利用关联函数乘积态捕获二维量子晶格系统中的长程关联
Capturing long range correlations in two-dimensional quantum lattice systems using correlator product states
摘要：
我们研究了关联函数产物态描述二维自旋模型基态性质的适用性。我们的多体波函数的anterior采取的形式无论是元格或债券关联函数产品的状态和能量优化通过使用Monte Carlo最小化改变的anterior。对于伊辛模型，我们发现，plaquette模型是最好的估计的能量，而债券模型捕捉预期的长程相关性和系统的临界行为更忠实。然而，对于反铁磁海森堡模型，plaquettes在描述局部和长程相关性方面优于键耦合器，因为它们包含大量的局部参数。这些观测结果对将关联函数的乘积态应用于其他更复杂系统具有定量意义，并给出了重要的启发性见解：特别是仔细地根据所考虑的系统、其相互作用和对称性来选择关联函数的必要性。
英文摘要：
We study the suitability of correlator product states for describing ground-state properties of two-dimensional spin models. Our ansatz for the many-body wave function takes the form of either plaquette or bond correlator product states and the energy is optimized by varying the correlators using Monte Carlo minimization. For the Ising model we find that plaquette correlators are best for estimating the energy while bond correlators capture the expected long-range correlations and critical behavior of the system more faithfully. For the antiferromagnetic Heisenberg model, however, plaquettes outperform bond correlators at describing both local and long-range correlations because of the substantially larger number of local parameters they contain. These observations have quantitative implications for the application of correlator product states to other more complex systems, and give important heuristic insights: in particular the necessity of carefully tailoring the choice of correlators to the system considered, its interactions and symmetries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
