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

TL;DR

This paper introduces a scalable neural network-based algorithm for Hamiltonian tomography in large, out-of-equilibrium quantum systems, enabling precise reconstruction of Hamiltonians with fewer measurements.

Contribution

The authors develop a neural network approach that efficiently performs Hamiltonian learning in large-scale, out-of-equilibrium quantum systems, demonstrated on ultracold atoms in optical lattices.

Findings

Successfully reconstructs Hamiltonians of large quasi-1D bosonic systems.

Significantly improves parameter precision over previous methods.

Operates with an accessible amount of experimental measurements.

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.