Seeding neural network quantum states with tensor network states
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908616301215744 |
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| author | Kaneko, Ryui Goto, Shimpei |
| author_facet | Kaneko, Ryui Goto, Shimpei |
| contents | We find an efficient approach to approximately convert matrix product states (MPSs) into restricted Boltzmann machine wave functions consisting of a multinomial hidden unit through a canonical polyadic (CP) decomposition of the MPSs. This method allows us to generate well-behaved initial neural network quantum states for quantum many-body ground-state calculations in polynomial time of the number of variational parameters and systematically shorten the distance between the initial states and the ground states while increasing the rank of the CP decomposition. We demonstrate the efficiency of our method by taking the transverse-field Ising model as an example and discuss possible applications of our method to more general quantum many-body systems in which the ground-state wave functions possess complex nodal structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeding neural network quantum states with tensor network states Kaneko, Ryui Goto, Shimpei Strongly Correlated Electrons Machine Learning Numerical Analysis Quantum Physics We find an efficient approach to approximately convert matrix product states (MPSs) into restricted Boltzmann machine wave functions consisting of a multinomial hidden unit through a canonical polyadic (CP) decomposition of the MPSs. This method allows us to generate well-behaved initial neural network quantum states for quantum many-body ground-state calculations in polynomial time of the number of variational parameters and systematically shorten the distance between the initial states and the ground states while increasing the rank of the CP decomposition. We demonstrate the efficiency of our method by taking the transverse-field Ising model as an example and discuss possible applications of our method to more general quantum many-body systems in which the ground-state wave functions possess complex nodal structures. |
| title | Seeding neural network quantum states with tensor network states |
| topic | Strongly Correlated Electrons Machine Learning Numerical Analysis Quantum Physics |
| url | https://arxiv.org/abs/2506.23550 |