Quantum Hamiltonian Learning for the Fermi-Hubbard Model
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911862182903808 |
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| author | Ni, Hongkang Li, Haoya Ying, Lexing |
| author_facet | Ni, Hongkang Li, Haoya Ying, Lexing |
| contents | This work proposes a protocol for Fermionic Hamiltonian learning. For the Hubbard model defined on a bounded-degree graph, the Heisenberg-limited scaling is achieved while allowing for state preparation and measurement errors. To achieve $ε$-accurate estimation for all parameters, only $\tilde{\mathcal{O}}(ε^{-1})$ total evolution time is needed, and the constant factor is independent of the system size. Moreover, our method only involves simple one or two-site Fermionic manipulations, which is desirable for experiment implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17390 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantum Hamiltonian Learning for the Fermi-Hubbard Model Ni, Hongkang Li, Haoya Ying, Lexing Quantum Physics Numerical Analysis This work proposes a protocol for Fermionic Hamiltonian learning. For the Hubbard model defined on a bounded-degree graph, the Heisenberg-limited scaling is achieved while allowing for state preparation and measurement errors. To achieve $ε$-accurate estimation for all parameters, only $\tilde{\mathcal{O}}(ε^{-1})$ total evolution time is needed, and the constant factor is independent of the system size. Moreover, our method only involves simple one or two-site Fermionic manipulations, which is desirable for experiment implementation. |
| title | Quantum Hamiltonian Learning for the Fermi-Hubbard Model |
| topic | Quantum Physics Numerical Analysis |
| url | https://arxiv.org/abs/2312.17390 |