The evolution of a closed quantum system is governed by a Hamiltonian operator. Learning the Hamiltonian is a central task in quantum metrology, sensing, and device characterization. In this talk I will first give an overview of existing Hamiltonian learning methods, and highlight two features that are surprising from a classical parameter estimation perspective: (1) the attainment of the Heisenberg-limited scaling and (2) the robustness against state preparation and measurement (SPAM) errors. These advantages rely heavily on quantum control, but existing protocols rely on control operations that are experimentally difficult. More precisely, they either require multi-qubit operations that are prone to noise, or single-qubit operations whose frequency or strength increases with the desired precision. I will introduce a recently proposed SPAM-robust protocol (arXiv:2601.10380) that learns a quantum Hamiltonian with the optimal Heisenberg-limited scaling using only single-qubit control in the form of static fields with strengths that are independent of the target precision. By overcoming these limitations, our protocol provides new tools for device characterization and quantum sensing.