Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of nonstoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pretrained 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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• Strongly correlated systems
• Approximation methods for many-body systems
• Machine learning
• Spin lattice models
• Variational wave functional methods