Near-Optimal Quantum Algorithm for Minimizing the Maximal Loss

The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(Nε^{-2/3} + ε^{-8/3})$ queries to a first-order oracle to compute an $ε$-suboptimal point. On the other hand, quantum algorithms for optimization are rapidly advancing with speedups shown on many important optimization problems. In this paper, we conduct a systematic study for quantum algorithms and lower bounds for minimizing the maximum of $N$ convex, Lipschitz functions. On one hand, we develop quantum algorithms with an improved complexity bound of $\tilde{O}(\sqrt{N}ε^{-5/3} + ε^{-8/3})$. On the other hand, we prove that quantum algorithms must take $\tildeΩ(\sqrt{N}ε^{-2/3})$ queries to a first order quantum oracle, showing that our dependence on $N$ is optimal up to poly-logarithmic factors.