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

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.

13 pages, 13 figures, code: https://gitlab.com/QMAI/papers/manybodydynlearning

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• Many-Body Physics with Ultracold Gases
• Probing many-body dynamics on a 51-atom quantum simulator
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• Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
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• Learning and simulating bosonic systems via finite-energy locality