Improving neural network performance for solving quantum sign structure

• URL: http://arxiv.org/abs/2510.02051v1
• Date: Thu, 02 Oct 2025 14:24:28 GMT
• Title: Improving neural network performance for solving quantum sign structure
• Authors: Xiaowei Ou, Tianshu Huang, Vidvuds Ozolins,
• Abstract summary: We introduce a modified method that effectively uses differing imaginary time steps to evolve the amplitude and phase.<n>Using a larger time step for phase reconfiguration, this method enables a simultaneous and efficient training of phase and amplitude neural networks.
• Score: 0.4806505912512236
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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