Code and Data for our Paper Scalably learning quantum many-body Hamiltonians from dynamical data

This repository serves as a reference point on how to use the code used in our paper. It also contains instructions on how to inspect or reproduce our numerical results.

The Differentiable TEBD Package

For our numerical analysis we developed a differentiable implementation of the TEBD which is contained in the standalone Python package differentiable-tebd.

Demo

This repository contains a brief demo on how to generate synthetic measurement data and then learn the Hamiltonian from it.

Numerical Results

The numerical results presented in the paper are contained in the data repository. To analyze the scaling of the error in the number of measurement samples we had to generate a large amount of synthetic data. Therefore, the total size of the data repository is about 80GB (37GB compressed).

Citing

If you use the differentiable-tebd package or any of our numerical results, please cite our paper.

@misc{wilde_scalably_2022,
  doi = {10.48550/ARXIV.2209.14328},
  url = {https://arxiv.org/abs/2209.14328},
  author = {Wilde, Frederik and Kshetrimayum, Augustine and Roth, Ingo and Hangleiter, Dominik and Sweke, Ryan and Eisert, Jens},
  title = {Scalably learning quantum many-body Hamiltonians from dynamical data},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}