论文标题
量子多粒子状态的混合卷积神经网络和PEPS波函数
Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states
论文作者
论文摘要
神经网络已被用作量子多粒子问题的变分波函数。已经表明，正确的符号结构对于获得高精确的基态能量至关重要。在这项工作中，我们提出了结合卷积神经网络（CNN）和预测的纠缠对状态（PEPS）的混合波函数，其中符号结构由PEPS确定，并且波浪功能的振幅由CNN提供。我们在高度沮丧的Spin-1/2 $ J_1 $ - $ J_2 $模型上基于ANSATZ进行基准测试。我们表明，实现的地面能量与最先进的结果具有竞争力。
Neural networks have been used as variational wave functions for quantum many-particle problems. It has been shown that the correct sign structure is crucial to obtain the high accurate ground state energies. In this work, we propose a hybrid wave function combining the convolutional neural network (CNN) and projected entangled pair states (PEPS), in which the sign structures are determined by the PEPS, and the amplitudes of the wave functions are provided by CNN. We benchmark the ansatz on the highly frustrated spin-1/2 $J_1$-$J_2$ model. We show that the achieved ground energies are competitive to state-of-the-art results.
