论文标题
核多体问题的隐藏核神经网络量子状态
Hidden-nucleons neural-network quantum states for the nuclear many-body problem
论文作者
论文摘要
我们将隐藏的神经网络量子状态的隐藏式特性家族涵盖，以涵盖连续和离散的自由度，并以系统地改进的方式解决核多体schrödinger方程。我们证明，与Slater-Jastrow Ansatz相比，在原始希尔伯特空间中添加隐藏的核子大大增强了神经网络结构的表达。还讨论了在波函数点对称（例如奇偶校验和时间逆转）中明确编码的好处。为了利用改进的优化方法和采样技术，Hidden-Nucleon Ansatz的精度与光核中的数值高度谐音和辅助场扩散Monte Carlo在$^{16} $ o中相当。由于其多项式缩放与核子的数量，该方法为中等质量核的高度精确的量子蒙特卡洛研究开辟了道路。
We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrödinger equation in a systematically improvable fashion. We demonstrate that adding hidden nucleons to the original Hilbert space considerably augments the expressivity of the neural-network architecture compared to the Slater-Jastrow ansatz. The benefits of explicitly encoding in the wave function point symmetries such as parity and time-reversal are also discussed. Leveraging on improved optimization methods and sampling techniques, the hidden-nucleon ansatz achieves an accuracy comparable to the numerically-exact hyperspherical harmonic method in light nuclei and to the auxiliary field diffusion Monte Carlo in $^{16}$O. Thanks to its polynomial scaling with the number of nucleons, this method opens the way to highly-accurate quantum Monte Carlo studies of medium-mass nuclei.
