Learning many-body Hamiltonians with Heisenberg-limited scaling
This solves a fundamental problem in physics for quantum simulation and information processing, offering a significant speedup over prior algorithms.
They tackled the problem of learning many-body Hamiltonians from dynamics by proposing the first algorithm to achieve Heisenberg-limited scaling, estimating parameters to ε-error with O(ε⁻¹) evolution time and polylog(ε⁻¹) experiments, compared to previous methods requiring O(ε⁻²).
Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this work, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting $N$-qubit local Hamiltonian. After a total evolution time of $\mathcal{O}(ε^{-1})$, the proposed algorithm can efficiently estimate any parameter in the $N$-qubit Hamiltonian to $ε$-error with high probability. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses $\mathrm{polylog}(ε^{-1})$ experiments. In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(ε^{-2})$ and $\mathcal{O}(ε^{-2})$ experiments. 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. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.