论文标题
自然进化策略和变异蒙特卡洛
Natural evolution strategies and variational Monte Carlo
论文作者
论文摘要
引入了量子自然进化策略的概念，该策略提供了许多用于执行经典黑盒优化的已知量子/经典算法的几何综合。 Gomes等人的最新工作。 [2019]在这种情况下，对使用神经量子状态的启发式组合优化进行了教学审查，强调了与自然进化策略的联系。说明了算法框架，以解决近似组合优化问题，并找到了改善近似比的系统策略。特别是发现，自然进化策略可以实现近似比的竞争，并以广泛使用的启发式算法的最大切割算法，而牺牲了计算时间的增加。
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. Recent work of Gomes et al. [2019] on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies. 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 natural evolution strategies can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time.
