Scalably learning quantum many-body Hamiltonians from dynamical data

Understand

The physics of a closed quantum mechanical system is governed by its Hamiltonian.

• However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system.
• In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efficient quantum state representations in terms of tensor networks.
• Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins.