Using Shadows to Learn Ground State Properties of Quantum Hamiltonians

Predicting properties of the ground state of a given quantum Hamiltonian is an important task central to various fields of science. Recent theoretical results show that for this task learning algorithms enjoy an advantage over non-learning algorithms for a wide range of important Hamiltonians. This work investigates whether the graph structure of these Hamiltonians can be leveraged for the design of sample efficient machine learning models. We demonstrate that corresponding Graph Neural Networks do indeed exhibit superior sample efficiency. Our results provide guidance in the design of machine learning models that learn on experimental data from near-term quantum devices.