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Autores principales: Sinha, Savar D., Tong, Yu
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2509.07937
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author Sinha, Savar D.
Tong, Yu
author_facet Sinha, Savar D.
Tong, Yu
contents We consider the problem of learning an $M$-sparse Hamiltonian and the related problem of Hamiltonian sparsity testing. Through a detailed analysis of Bell sampling, we reduce the total evolution time required by the state-of-the-art algorithm for $M$-sparse Hamiltonian learning to $\widetilde{\mathcal{O}}(M/ε)$, where $ε$ denotes the $\ell^{\infty}$ error, achieving an improvement by a factor of $M$ (ignoring the logarithmic factor) while only requiring access to forward time-evolution. We then establish a connection between Hamiltonian learning and Hamiltonian sparsity testing through Bell sampling, which enables us to propose a Hamiltonian sparsity testing with state-of-the-art total evolution time scaling.
format Preprint
id arxiv_https___arxiv_org_abs_2509_07937
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Improved Hamiltonian learning and sparsity testing through Bell sampling
Sinha, Savar D.
Tong, Yu
Quantum Physics
We consider the problem of learning an $M$-sparse Hamiltonian and the related problem of Hamiltonian sparsity testing. Through a detailed analysis of Bell sampling, we reduce the total evolution time required by the state-of-the-art algorithm for $M$-sparse Hamiltonian learning to $\widetilde{\mathcal{O}}(M/ε)$, where $ε$ denotes the $\ell^{\infty}$ error, achieving an improvement by a factor of $M$ (ignoring the logarithmic factor) while only requiring access to forward time-evolution. We then establish a connection between Hamiltonian learning and Hamiltonian sparsity testing through Bell sampling, which enables us to propose a Hamiltonian sparsity testing with state-of-the-art total evolution time scaling.
title Improved Hamiltonian learning and sparsity testing through Bell sampling
topic Quantum Physics
url https://arxiv.org/abs/2509.07937