Optimal short-time measurements for Hamiltonian learning

Netanel Lindner

Characterizing noisy quantum devices requires methods for learning the underlying quantum Hamiltonian which governs their dynamics. Often, such methods compare measurements to simulations of candidate Hamiltonians, a task which requires exponential computational complexity. We propose efficient measurement scheme based on short-time dynamics which circumvent this exponential difficulty. We provide a method for estimating the optimal measurement schedule and the consequent reconstruction error, and verify these estimates numerically. We demonstrate that the reconstruction requires a system-size independent number of experimental shots, and identify a minimal set of state preparations and measurements which yields optimal accuracy for learning short-ranged Hamiltonians.