Quantum Hamiltonian Learning for the Fermi-Hubbard Model

Hongkang Ni; Haoya Li; Lexing Ying

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arXiv · 2312.17390

Quantum Hamiltonian Learning for the Fermi-Hubbard Model

Hongkang Ni·Haoya Li·Lexing Ying

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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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Hongkang Ni, Haoya Li, Lexing Ying. 2023-12-28. Quantum Hamiltonian Learning for the Fermi-Hubbard Model. https://arxiv.org/abs/2312.17390

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