Learning the unknown Hamiltonian governing the dynamics of a quantum many-body system is a challenging task. In this manuscript, we propose a possible strategy based on repeated measurements on a single time-dependent state. We prove that the accuracy of the learning process is maximized for states that are delocalized in the Hamiltonian eigenbasis. This implies that delocalization is a quantum resource for Hamiltonian learning, that can be exploited to select optimal initial states for learning algorithms. We investigate the error scaling of our reconstruction with respect to the number of measurements, and we provide examples of our learning algorithm on simulated quantum systems.

High-accuracy Hamiltonian learning via delocalized quantum state evolutions / Rattacaso, D.; Passarelli, G.; Lucignano, P.. - In: QUANTUM. - ISSN 2521-327X. - 7:(2023). [10.22331/q-2023-01-26-905]

High-accuracy Hamiltonian learning via delocalized quantum state evolutions

Rattacaso D.;Passarelli G.;Lucignano P.
2023

Abstract

Learning the unknown Hamiltonian governing the dynamics of a quantum many-body system is a challenging task. In this manuscript, we propose a possible strategy based on repeated measurements on a single time-dependent state. We prove that the accuracy of the learning process is maximized for states that are delocalized in the Hamiltonian eigenbasis. This implies that delocalization is a quantum resource for Hamiltonian learning, that can be exploited to select optimal initial states for learning algorithms. We investigate the error scaling of our reconstruction with respect to the number of measurements, and we provide examples of our learning algorithm on simulated quantum systems.
2023
High-accuracy Hamiltonian learning via delocalized quantum state evolutions / Rattacaso, D.; Passarelli, G.; Lucignano, P.. - In: QUANTUM. - ISSN 2521-327X. - 7:(2023). [10.22331/q-2023-01-26-905]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/949845
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