Publications [#378593] of Yu Tong

Papers Published

1. Huang, H-Y; Tong, Y; Fang, D; Su, Y, Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling., Physical review letters, vol. 130 no. 20 (May, 2023), pp. 200403 [doi]
   (last updated on 2024/11/20)

   Abstract:
   Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this Letter, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting N-qubit local Hamiltonian. After a total evolution time of O(ε^{-1}), the proposed algorithm can efficiently estimate any parameter in the N-qubit Hamiltonian to ε error with high probability. Our algorithm uses ideas from quantum simulation to decouple the unknown N-qubit Hamiltonian H into noninteracting patches and learns H using a quantum-enhanced divide-and-conquer approach. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses polylog(ε^{-1}) experiments. In contrast, the best existing algorithms require O(ε^{-2}) experiments and total evolution time. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.