Quantum Search with Prior Knowledge

TL;DR

This paper introduces a new quantum search algorithm that leverages prior knowledge to outperform standard Grover's algorithm on average, achieving optimal success probability with a fixed number of queries.

Contribution

A novel generalization of Grover's algorithm that incorporates prior probability distributions to improve average-case performance.

Findings

Outperforms standard Grover's algorithm on average

Achieves optimal expected success probability

Effective when prior knowledge is available

Abstract

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.