Active Learning of Quantum System Hamiltonians yields Query Advantage

Understand

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers.

• Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources.
• Standard techniques for Hamiltonian learning require careful design of queries and $O(\epsilon^{-2})$ queries in achieving learning error $\epsilon$ due to the standard quantum limit.
• With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error $\epsilon$ through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data.