📅 This Week in Quantum Machine Learning – Chippr Robotics

With the fast development of quantum technology, the size of quantum systems we can digitally manipulate and analogly probe increase drastically. In order to have a 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 or dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information of the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a practical and efficient Hamiltonian learning method that circumvents these limitations. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short time dynamics and local operations without any information of the Hamiltonian or preparing any eigenstates or thermal states. The method has scalable complexity and vanishing failure probability regarding the qubit number.