论文标题
量子海森堡链的生成神经抽样器
Generative Neural Samplers for the Quantum Heisenberg Chain
论文作者
论文摘要
生成神经抽样器为统计物理和量子场理论问题的蒙特卡洛方法提供了一种补充方法。这项工作测试了生成神经抽样器估计现实世界中低维自旋系统可观察到的能力。它绘制出自回旋模型如何通过基于Suzuki-Trotter转换的经典近似来对量子Heisenberg链进行采样。我们提出了各向同性XXX和各向异性XY链的能量，比热和敏感性的结果，这些链与蒙特卡洛在相同的近似方案中非常吻合。
Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory. This work tests the ability of generative neural samplers to estimate observables for real-world low-dimensional spin systems. It maps out how autoregressive models can sample configurations of a quantum Heisenberg chain via a classical approximation based on the Suzuki-Trotter transformation. We present results for energy, specific heat and susceptibility for the isotropic XXX and the anisotropic XY chain that are in good agreement with Monte Carlo results within the same approximation scheme.
