论文标题
$ \ mathbf {a \ leq 4} $ nuclei的变异蒙特卡洛计算带有人工神经网络相关器ansatz
Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with an artificial neural-network correlator ansatz
论文作者
论文摘要
多体量子波函数的复杂性是多个物理和化学领域的核心方面，在这些领域中，非扰动相互作用是突出的。事实证明，人工神经网络（ANN）是在凝结物质和化学问题中近似量子多体状态的灵活工具。在这项工作中，我们引入了一个神经网络量子状态ansatz，以模拟光核的地面波函数，并大致求解了核多体schrödinger方程。使用有效的随机采样和优化方案，我们的方法扩展了ANN在现场的开创性应用，这些应用程序呈指数级缩放算法复杂性。我们计算出$ a \ leq 4 $ nuclei的结合能和点核密度，从领先的无效有效的田间理论哈密顿式出现。我们成功地基于基于两体和三体jastrow函数的更常规的参数化基准基准测试ANN波函数，并且实际上是脱离了Green的函数蒙特卡洛的结果。
The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrödinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially-scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of $A\leq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually-exact Green's function Monte Carlo results.
