A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. The recent work of Gomes et al (2019 arXiv:1910.10675) on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies (NES). The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular, it is found that NES can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time.

引入了量子自然演化策略的概念，该概念提供了用于执行经典黑盒优化的许多已知量子/经典算法的几何合成。Gomes等人（2019 arXiv：1910.10675）在使用神经量子态的启发式组合优化方面的最新工作在此背景下进行了教学研究，强调了与自然进化策略（NES）的联系。说明了近似组合优化问题的算法框架，并找到了一种提高近似率的系统策略。尤其是，发现NES可以实现与广泛使用的Max-Cut启发式算法相比具有竞争优势的逼近比，但要增加计算时间。