标题： 量子辅助遗传算法 显示英文标题

标题： Quantum-Assisted Genetic Algorithm

摘要： 遗传算法通过模仿进化过程来解决优化问题，可以通过使用强大的半局部搜索算法作为变异算子来增强。 在这里，我们引入反向量子退火，这是一种可以用于从经典状态开始执行一系列准局部或准非局部搜索的量子演化类别，作为新的变异来源。 反向退火使得能够开发使用量子涨落进行变异和使用经典机制进行交叉的遗传算法——我们将这些称为量子辅助遗传算法（QAGAs）。 我们描述了一个QAGA，并展示了使用D-Wave 2000Q量子退火处理器的实验结果。 在一组自旋玻璃输入上，标准（正向）量子退火能够快速找到好的解决方案，但难以找到全局最优解。 相比之下，我们的QAGA在这些输入上有效地找到了全局最优解。 这种非局部经典和量子涨落的成功协同可能为噪声中等规模量子（NISQ）设备在启发式离散优化中的实际应用提供有希望的一步。 显示英文摘要

摘要： Genetic algorithms, which mimic evolutionary processes to solve optimization problems, can be enhanced by using powerful semi-local search algorithms as mutation operators. Here, we introduce reverse quantum annealing, a class of quantum evolutions that can be used for performing families of quasi-local or quasi-nonlocal search starting from a classical state, as novel sources of mutations. Reverse annealing enables the development of genetic algorithms that use quantum fluctuation for mutations and classical mechanisms for the crossovers -- we refer to these as Quantum-Assisted Genetic Algorithms (QAGAs). We describe a QAGA and present experimental results using a D-Wave 2000Q quantum annealing processor. On a set of spin-glass inputs, standard (forward) quantum annealing finds good solutions very quickly but struggles to find global optima. In contrast, our QAGA proves effective at finding global optima for these inputs. This successful interplay of non-local classical and quantum fluctuations could provide a promising step toward practical applications of Noisy Intermediate-Scale Quantum (NISQ) devices for heuristic discrete optimization.