Sparse Hamiltonians can be learned with slower quantum controls

Researchers at the Korea Advanced Institute of Science and Technology have demonstrated Heisenberg-limited Hamiltonian learning without relying on access to arbitrarily short-time dynamics, a previously critical requirement for efficient quantum system characterization. The work introduces a framework where queries to a quantum system’s dynamics have a minimum duration of time T, yet still achieve optimal scaling for learning the system’s Hamiltonian. For logarithmically sparse Hamiltonians, the algorithm achieves the information-theoretically optimal 1/ε scaling in total evolution time for any arbitrary constant minimum evolution time T. The results reveal that high-bandwidth, ultra-short…