An efficient quantum search engine on unsorted database

TL;DR

This paper presents a quantum search engine for unsorted databases that efficiently handles complex queries by extending the factorized quantum search algorithm with auxiliary files, making quantum database management feasible.

Contribution

It introduces a method to adapt the factorized quantum search algorithm for non-distinct property values using auxiliary files, enabling efficient complex query processing.

Findings

Query complexity is O(P*Q*M*log_{4}N).

The approach makes quantum database management feasible and efficient.

Supports complex queries on unsorted databases using quantum algorithms.

Abstract

We consider the problem of finding one or more desired items out of an unsorted database. Patel has shown that if the database permits quantum queries, then mere digitization is sufficient for efficient search for one desired item. The algorithm, called factorized quantum search algorithm, presented by him can locate the desired item in an unsorted database using $O(log_{4}N)$ queries to factorized oracles. But the algorithm requires that all the property values must be distinct from each other. In this paper, we discuss how to make a database satisfy the requirements, and present a quantum search engine based on the algorithm. Our goal is achieved by introducing auxiliary files for the property values that are not distinct, and converting every complex query request into a sequence of calls to factorized quantum search algorithm. The query complexity of our algorithm is $O(P*Q*M*log_{4}N)$, where P is the number of the potential simple query requests in the complex query request, Q is the maximum number of calls to the factorized quantum search algorithm of the simple queries, M is the number of the auxiliary files for the property on which our algorithm are searching for desired items. This implies that to manage an unsorted database on an actual quantum computer is possible and efficient.