Optimal Linear Combination of Hamiltonian Simulation for Dissipative Dynamics

Abstract

Linear combination of Hamiltonian simulation (LCHS) is an efficient method for simulating dissipative dynamics on quantum computers. The choice of the LCHS kernel controls the cost of the algorithm. We prove that the kernel family proposed by Ni and Ying~\cite{NY26} minimizes the LCHS query cost in the minimax sense. For target accuracy $ε$, the minimum cost is asymptotically $\frac{2\mathrm{e}}π(\ln\frac1ε-\ln\ln\frac1ε+o(1))$, with optimal leading constant $\frac{2\mathrm{e}}π$. Our lower-bound proof combines weak duality with a realization lemma that approximates extremal dual functions by matrix elements of dissipative evolutions.