Quantum partial search for uneven distribution of multiple target items

TL;DR

This paper studies an optimized quantum partial search algorithm for multiple target items unevenly distributed across a database, improving efficiency by analyzing distribution effects and optimizing query strategies.

Contribution

It introduces an optimized quantum partial search algorithm tailored for uneven target distributions and analyzes how distribution affects search efficiency.

Findings

Algorithm runs fastest with evenly distributed target items.

Optimization improves query efficiency for uneven distributions.

Perturbation analysis guides distribution-aware optimization.

Abstract

Quantum partial search algorithm is approximate search. It aims to find a target block (which has the target items). It runs a little faster than full Grover search. In this paper, we consider quantum partial search algorithm for multiple target items unevenly distributed in database (target blocks have different number of target items). The algorithm we describe can locate one of the target blocks. Efficiency of the algorithm is measured by number of queries to the oracle. We optimize the algorithm in order to improve efficiency. By perturbation method, we find that the algorithm runs the fastest when target items are evenly distributed in database.