论文标题
具有先验知识的量子搜索
Quantum Search with Prior Knowledge
论文作者
论文摘要
搜索基本算法在不同的情况下具有广泛的应用程序。 Grover的量子搜索算法及其概括，幅度放大，为非结构化搜索提供了二次加速。我们考虑使用先验知识搜索的问题。更明确地，在具有先前概率分布的n个项目之间搜索解决方案。这封信提出了对Grover的搜索算法的新概括，该算法的性能优于标准的Grover算法在此设置下。我们证明，如果固定查询数量，我们的新算法实现了找到解决方案的最佳预期成功概率。
Search-base algorithms have widespread applications in different scenarios. Grover's quantum search algorithms and its generalization, amplitude amplification, provide a quadratic speedup over classical search algorithms for unstructured search. We consider the problem of searching with prior knowledge. More preciously, search for the solution among N items with a prior probability distribution. This letter proposes a new generalization of Grover's search algorithm which performs better than the standard Grover algorithm in average under this setting. We prove that our new algorithm achieves the optimal expected success probability of finding the solution if the number of queries is fixed.
