Weighted Approximate Quantum Natural Gradient for Variational Quantum Eigensolver

The variational quantum eigensolver (VQE) is one of the most prominent algorithms using near-term quantum devices, designed to find the ground state of a Hamiltonian. In VQE, a classical optimizer iteratively updates the parameters in the quantum circuit. Among various optimization methods, the quantum natural gradient descent (QNG) stands out as a promising optimization approach for VQE. However, standard QNG only leverages the quantum Fisher information of the entire system and treats each subsystem equally in the optimization process, without accounting for the different weights and contributions of each subsystem corresponding to each local term in the Hamiltonian. To address this limitation, we propose a Weighted Approximate Quantum Natural Gradient (WA-QNG) method tailored for $k$-local Hamiltonians. In this paper, we theoretically analyze the potential advantages of WA-QNG compared to QNG from three distinct perspectives and reveal its connection with the Gauss-Newton method. We also show it outperforms the standard quantum natural gradient descent in the numerical simulations for seeking the ground state of the Hamiltonian.

💡 Research Summary

The paper addresses a fundamental limitation of the standard Quantum Natural Gradient (QNG) method when applied to the Variational Quantum Eigensolver (VQE). While QNG leverages the quantum Fisher information matrix (F) of the entire quantum state to pre‑condition gradient updates, it treats every part of the system uniformly and ignores the structure of the Hamiltonian, which in most practical VQE problems is expressed as a sum of k‑local terms (H = \sum_m h_m H_m). Each local term acts on a small subsystem and contributes to the total energy with a weight (h_m). Consequently, the sensitivity of the objective function (f(\theta)=\operatorname{Tr}