标题： 改进神经网络在解决量子符号结构中的性能 显示英文标题

标题： Improving neural network performance for solving quantum sign structure

摘要： 神经量子态已成为研究非马尔可夫哈密顿量基态的广泛应用方法。 然而，现有方法通常依赖于对符号结构的先验知识，或需要单独预训练的相位网络。 我们引入了一种改进的随机再配置方法，该方法有效利用不同的虚时间步长来演化振幅和相位。 使用较大的时间步长进行相位优化，此方法实现了相位和振幅神经网络的同时高效训练。 我们的方法在海森堡 J_1-J_2 模型上得到了验证。 显示英文摘要

摘要： Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-trained phase network. We introduce a modified stochastic reconfiguration method that effectively uses differing imaginary time steps to evolve the amplitude and phase. Using a larger time step for phase optimization, this method enables a simultaneous and efficient training of phase and amplitude neural networks. The efficacy of our method is demonstrated on the Heisenberg J_1-J_2 model.