Low Depth Distributed Quantum Algorithms for Unordered Database Search

TL;DR

This paper introduces a low-depth distributed quantum search algorithm suitable for NISQ devices, reducing circuit complexity and noise impact while accurately locating targets in unordered databases.

Contribution

It presents a novel distributed quantum search method that lowers circuit depth and enhances noise resistance compared to existing algorithms.

Findings

The algorithm reduces circuit depth and error accumulation.

Experiments confirm the algorithm's effectiveness and noise resistance.

The method accurately locates targets in unordered databases.

Abstract

Grover's algorithm accelerates unstructured database search quadratically compared to classical algorithms. In the NISQ era, distributed quantum computing can decrease circuit depth and reduce noise. In this paper, an algorithm for constructing query operators for subfunctions is proposed. By dividing the target string of the search problem into several substrings and integrating the query operator of each subfunction, a low-depth distributed exact quantum search algorithm is designed. The contributions of this paper are as follows: (1) The proposed distributed algorithm has a lower circuit depth and can mitigate error accumulation compared to distributed quantum search algorithms; (2) The target can be accurately located by the proposed distributed algorithm; (3) Experiments conducted with the quantum software MindQuantum confirm the effectiveness and feasibility of the proposed distributed algorithm. Moreover, the introduction of noise to the circuit during these experiments indicates that the algorithm possesses an inherent capacity for noise resistance.