Complementary-multiphase quantum search for all numbers of target items

TL;DR

This paper introduces a complementary-multiphase quantum search algorithm that maintains high success probability across all target item fractions, improving efficiency over existing methods especially for high success probabilities.

Contribution

The paper proposes a novel complementary-multiphase quantum search algorithm that works for all target fractions and achieves high success probability with fewer iterations than previous algorithms.

Findings

Achieves success probability above any specified threshold between 0 and 1.

Applicable to the entire range of target item fractions , .

Reduces the number of iterations compared to existing algorithms, especially for high success probabilities.

Abstract

Grover's algorithm achieves a quadratic speedup over classical algorithms, but it is considered necessary to know the value of $\lambda$ exactly [Phys. Rev. Lett. 95, 150501 (2005); Phys. Rev. Lett. 113, 210501 (2014)], where $\lambda$ is the fraction of target items in the database. In this paper, we find out that the Grover algorithm can actually apply to the case where one can identify the range that $\lambda$ belongs to from a given series of disjoint ranges. However, Grover's algorithm still cannot maintain high success probability when there exist multiple target items. For this problem, we proposed a complementary-multiphase quantum search algorithm, %with general iterations, in which multiple phases complement each other so that the overall high success probability can be maintained. Compared to the existing algorithms, in the case defined above, for the first time our algorithm achieves the following three goals simultaneously: (1) the success probability can be no less than any given value between 0 and 1, (2) the algorithm is applicable to the entire range of $\lambda$, and (3) the number of iterations is almost the same as that of Grover's algorithm. Especially compared to the optimal fixed-point algorithm [Phys. Rev. Lett. 113, 210501 (2014)], our algorithm uses fewer iterations to achieve success probability greater than 82.71\%, e.g., when the minimum success probability is required to be 99.25\%, the number of iterations can be reduced by 50\%.