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arXiv · 2103.01240

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

Agnes Valenti·Guliuxin Jin·Julian Léonard·Sebastian D. Huber·Eliska Greplova
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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 size quasi-1D bosonic system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.

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Agnes Valenti, Guliuxin Jin, Julian Léonard, Sebastian D. Huber, Eliska Greplova. 2021-03-01. Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics. https://doi.org/10.1103/physreva.105.023302

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