Quantum Hamiltonian Learning for the Fermi-Hubbard Model

• URL: http://arxiv.org/abs/2312.17390v2
• Date: Wed, 1 May 2024 20:23:12 GMT
• Title: Quantum Hamiltonian Learning for the Fermi-Hubbard Model
• Authors: Hongkang Ni, Haoya Li, Lexing Ying,
• Abstract summary: Heisenberg-limited scaling is achieved while allowing for state preparation and measurement errors. Our method only involves simple one or two-site Fermionic manipulations.
• Score: 10.391338066539237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\epsilon$-accurate estimation for all parameters, only $\tilde{\mathcal{O}}(\epsilon^{-1})$ 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.

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