Optimal learning of quantum Hamiltonians from high-temperature Gibbs states

We study the problem of learning a Hamiltonian H to precision \varepsilon, supposing we are given copies of its Gibbs state \rho =\exp(-\beta H)/\mathrm{Tr}(\exp(-\beta H)) at a known inverse temperature \beta. Anshu, Arunachalam, Kuwahara, and Soleimanifar [AAKS21] recently studied the sample compl...

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Bibliographic Details

Published in:	Proceedings / annual Symposium on Foundations of Computer Science pp. 135 - 146
Main Authors:	Haah, Jeongwan, Kothari, Robin, Tang, Ewin
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2022
Subjects:	Complexity theory Computer science Hamiltonian learning Markov random field partition function quantum algorithm Quantum computing real-time evolution Real-time systems Time complexity
ISSN:	2575-8454
Online Access:	Get full text
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