The quantum walk search algorithm: Factors affecting efficiency

TL;DR

This paper investigates how various structural factors like dimensionality, connectivity, and disorder influence the efficiency of the quantum walk search algorithm, revealing secondary dependencies beyond just spatial dimension.

Contribution

It extends previous studies by analyzing the effects of connectivity, symmetry, and disorder on the quantum walk search algorithm's performance.

Findings

Higher connectivity improves search efficiency

The algorithm tolerates a significant level of disorder

Secondary dependencies include lattice symmetry and structure

Abstract

We numerically study the quantum walk search algorithm of Shenvi, Kempe and Whaley [PRA \textbf{67} 052307] and the factors which affect its efficiency in finding an individual state from an unsorted set. Previous work has focused purely on the effects of the dimensionality of the dataset to be searched. Here, we consider the effects of interpolating between dimensions, connectivity of the dataset, and the possibility of disorder in the underlying substrate: all these factors affect the efficiency of the search algorithm. We show that, as well as the strong dependence on the spatial dimension of the structure to be searched, there are also secondary dependencies on the connectivity and symmetry of the lattice, with greater connectivity providing a more efficient algorithm. In addition, we also show that the algorithm can tolerate a non-trivial level of disorder in the underlying substrate.