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Improving neural network performance for solving quantum sign structure
Xiaowei Ou, Tianshu Huang, Vidvuds Ozolins·October 2, 2025·DOI: 10.1103/fqxr-r8vw
Quantum Physicscond-mat.str-elphysics.comp-ph
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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.