Sep 28, 2022 · In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data.

Sep 28, 2022 · In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical ...

Sep 30, 2022 · In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical ...

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.

Co-authors ; Scalably learning quantum many-body Hamiltonians from dynamical data. F Wilde, A Kshetrimayum, I Roth, D Hangleiter, R Sweke, J Eisert. arXiv ...

F. Wilde, Kshetrimayum, A., Roth, I., Hangleiter, D., Sweke, R., and Eisert, J., “Scalably learning quantum many-body Hamiltonians from dynamical data”, 2022.

Scalably learning quantum many-body Hamiltonians from dynamical data. F Wilde, A Kshetrimayum, I Roth, D Hangleiter, R Sweke, J Eisert. arXiv preprint arXiv ...

This package was used to generate the numerical results in our paper on Scalably learning quantum many-body Hamiltonians from dynamical data. Environment.

Jul 27, 2023 · Eisert, “Scalably learning quantum many-body Hamiltonians from dynamical data,” arXiv preprint arXiv:2209.14328, 2022. [39] E. Onorati, C ...

Scalably learning quantum many-body Hamiltonians from dynamical data · Recovering quantum gates from few average gate fidelities.