Robust and Efficient Hamiltonian Learning

Understand

With the fast development of quantum technology, the sizes of both digital and analog quantum systems increase drastically.

• In order to have better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static and dynamic properties of the system.
• Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information on the Hamiltonian structure and the ground or thermal states of the system.
• In this work, we present a robust and efficient Hamiltonian learning method that circumvents these limitations based only on mild assumptions.