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Practical Black Box Hamiltonian Learning. (arXiv:2206.15464v1 [quant-ph])
July 1, 2022, 1:10 a.m. | Andi Gu, Lukasz Cincio, Patrick J. Coles
cs.LG updates on arXiv.org arxiv.org
We study the problem of learning the parameters for the Hamiltonian of a
quantum many-body system, given limited access to the system. In this work, we
build upon recent approaches to Hamiltonian learning via derivative estimation.
We propose a protocol that improves the scaling dependence of prior works,
particularly with respect to parameters relating to the structure of the
Hamiltonian (e.g., its locality $k$). Furthermore, by deriving exact bounds on
the performance of our protocol, we are able to provide …
More from arxiv.org / cs.LG updates on arXiv.org
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