Paths towards time evolution with larger neural-network quantum states
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913378931310592 |
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| author | Zhang, Wenxuan Xing, Bo Xu, Xiansong Poletti, Dario |
| author_facet | Zhang, Wenxuan Xing, Bo Xu, Xiansong Poletti, Dario |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_03381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Paths towards time evolution with larger neural-network quantum states Zhang, Wenxuan Xing, Bo Xu, Xiansong Poletti, Dario Quantum Physics Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics 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. |
| title | Paths towards time evolution with larger neural-network quantum states |
| topic | Quantum Physics Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics |
| url | https://arxiv.org/abs/2406.03381 |