2021/03/01 by Agnes Valenti, Guliuxin Jin, Julian Léonard +3 · 34 citations
Computer Science · Mathematics · Physics and Astronomy · #Benchmarking #Cold Atom Physics and Bose-Einstein Condensates #Computational science #Computer science #Hamiltonian (control theory) #Mathematical optimization #Mathematics #Open quantum system #Physics #Quantum #Quantum Information and Cryptography #Quantum dynamics #Quantum many-body systems #Quantum mechanics #Quantum simulator #Quantum system #Scalability #Scale (ratio) #Statistical physics #Verifiable secret sharing #cond-mat.dis-nn #cond-mat.quant-gas #quant-ph
paper · pdf · doi:10.1103/physreva.105.023302
published in Physical Review A 105(2) (American Physical Society) · 13 pages, 13 figures, code: https://gitlab.com/QMAI/papers/manybodydynlearning
arxiv created 2021/03/01 · openalex publication_date 2022/02/01 · arxiv updated 2022/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.