  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. We formally specify and experimentally assess the
performance of this Hamiltonian active learning (HAL) algorithm for learning
the six parameters of a two-qubit cross-resonance Hamiltonian on four different
superconducting IBM Quantum devices. Compared with standard techniques for the
same problem and a specified learning error, HAL achieves up to a $99.8\%$
reduction in queries required, and a $99.1\%$ reduction over the comparable
non-adaptive learning algorithm. Moreover, with access to prior information on
a subset of Hamiltonian parameters and given the ability to select queries with
linearly (or exponentially) longer system interaction times during learning,
HAL can exceed the standard quantum limit and achieve Heisenberg (or
super-Heisenberg) limited convergence rates during learning.
