论文标题
遗传量子退火算法
A Genetic Quantum Annealing Algorithm
论文作者
论文摘要
遗传算法（GA）是一种基于遗传学和自然选择原理的基于搜索的优化技术。我们提出了一种算法，该算法通过量子退火器的输入来增强经典GA。与经典GA一样，该算法通过根据其适应性繁殖了一系列可能的解决方案来工作。但是，个体的种群由量子退火器上的连续耦合来定义，然后通过量子退火引起代表尝试溶液的相应表型的集合。这将一种定向突变的形式引入算法中，可以以各种方式增强其性能。两个关键的增强来自具有从父母的适应性（所谓的裙带关系）的连续耦合以及从退火耦合中继承的优势，从而使整个人群都受到最合适的个体（所谓的量子量子化）的影响。我们发现我们的算法在几个简单问题上比经典的GA更强大。
A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA with input from quantum annealers. As in a classical GA, the algorithm works by breeding a population of possible solutions based on their fitness. However, the population of individuals is defined by the continuous couplings on the quantum annealer, which then give rise via quantum annealing to the set of corresponding phenotypes that represent attempted solutions. This introduces a form of directed mutation into the algorithm that can enhance its performance in various ways. Two crucial enhancements come from the continuous couplings having strengths that are inherited from the fitness of the parents (so-called nepotism) and from the annealer couplings allowing the entire population to be influenced by the fittest individuals (so-called quantum-polyandry). We find our algorithm to be significantly more powerful on several simple problems than a classical GA.
