Optimal Ansatz-free Hamiltonian Learning In Situ

Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction. Recent works proposed protocols have achieved the optimal Heisenberg-limited scaling learning ansatz-free Hamiltonians from their real-time evolutions without fully specifying interaction structures. However, these protocols relies on both deep circuits with interleaving probes and control, and extremely short time resolution, making them difficult to implement on near- and intermediate-term in situ quantum experiments. In this work, we propose a computational efficient, *control-free*, and *ancilla-free* algorithm that uses only *Pauli product state preparation and measurement*, and learns an ansatz-free Hamiltonian $H$ with $||H||\leq\Lambda$ in total evolution time of $\Theta(\tfrac{\Lambda}{\epsilon^2}\log(\tfrac{\Lambda}{\epsilon}))$. The evolution time cost of our algorithm is *optimal* for any control-free protocols as we further prove a lower bound of $\Omega(\tfrac{\Lambda}{\epsilon^2}\log(\tfrac{\Lambda}{\epsilon}))$. Technically, our method introduces a randomized-sampling framework that combines band-limited kernel-based time sampling with a displacement sieve for Hamiltonian structure learning. The characteristic probe time resolution depends only on $\Lambda$ instead of $\varepsilon$, which makes our protocol especially appealing in the high-precision regime for sensing and calibration applications.