Paths towards time evolution with larger neural-network quantum states

• URL: http://arxiv.org/abs/2406.03381v1
• Date: Wed, 5 Jun 2024 15:32:38 GMT
• Title: Paths towards time evolution with larger neural-network quantum states
• Authors: Wenxuan Zhang, Bo Xing, Xiansong Xu, Dario Poletti,
• Abstract summary: We consider a quantum quench from the paramagnetic to the anti-ferromagnetic phase in the tilted Ising model. We show that for both types of networks, the projected time-dependent variational Monte Carlo (p-tVMC) method performs better than the non-projected approach.
• Score: 17.826631514127012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the neural-network quantum states method has been investigated to study the ground state and the time evolution of many-body quantum systems. Here we expand on the investigation and consider a quantum quench from the paramagnetic to the anti-ferromagnetic phase in the tilted Ising model. We use two types of neural networks, a restricted Boltzmann machine and a feed-forward neural network. We show that for both types of networks, the projected time-dependent variational Monte Carlo (p-tVMC) method performs better than the non-projected approach. We further demonstrate that one can use K-FAC or minSR in conjunction with p-tVMC to reduce the computational complexity of the stochastic reconfiguration approach, thus allowing the use of these techniques for neural networks with more parameters.

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