论文标题
神经量子状态的动力学采样器
Kinetic samplers for neural quantum states
论文作者
论文摘要
神经量子状态（NQS）是一类新型的变异多体波函数，在近似多种量子状态下非常灵活。 NQS ANSATZ的优化需要从平方波函数振幅定义的相应概率分布中进行采样。为此，我们建议使用动力学采样方案，并证明在许多重要情况下，这种方法导致的自相关时间比大都市 - 悬挂式采样算法要小得多，同时仍然允许易于实现晶格对称性（与自动性模型不同）。我们还使用统一的歧管近似和投影算法来构建马尔可夫链的二维等轴测嵌入，并表明动力学采样有助于获得希尔伯特空间基础的更均匀和更奇异的覆盖范围。
Neural quantum states (NQS) are a novel class of variational many-body wave functions that are very flexible in approximating diverse quantum states. Optimization of an NQS ansatz requires sampling from the corresponding probability distribution defined by squared wave function amplitude. For this purpose we propose to use kinetic sampling protocols and demonstrate that in many important cases such methods lead to much smaller autocorrelation times than Metropolis-Hastings sampling algorithm while still allowing to easily implement lattice symmetries (unlike autoregressive models). We also use Uniform Manifold Approximation and Projection algorithm to construct two-dimensional isometric embedding of Markov chains and show that kinetic sampling helps attain a more homogeneous and ergodic coverage of the Hilbert space basis.
