Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
Agnes Valenti (ETH Zürich)
G. Jin (ETH Zürich, TU Delft - QN/Greplová Lab, Kavli institute of nanoscience Delft)
Julian Léonard (Harvard University)
Sebastian D. Huber (ETH Zürich)
Eliska Greplova (Kavli institute of nanoscience Delft, TU Delft - QN/Greplová Lab)
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Abstract
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.