Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics

Agnes Valenti, Guliuxin Jin, Julian Léonard, Sebastian D. Huber, Eliska Greplova

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
9 Downloads (Pure)


Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large-scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary sized bosonic ladder system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.

Original languageEnglish
Article number023302
Number of pages14
JournalPhysical Review A
Issue number2
Publication statusPublished - 2022


Dive into the research topics of 'Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics'. Together they form a unique fingerprint.

Cite this