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

• URL: http://arxiv.org/abs/2103.01240v1
• Date: Mon, 1 Mar 2021 19:00:15 GMT
• Title: Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
• Authors: Agnes Valenti and Guliuxin Jin and Julian L\'eonard and Sebastian D. Huber and Eliska Greplova
• Abstract summary: We present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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