Approximating Edit Distance in Truly Subquadratic Time: Quantum and MapReduce

TL;DR

This paper presents quantum and MapReduce algorithms that approximate the edit distance between strings more efficiently than previous methods, achieving subquadratic runtime with constant or near-constant approximation factors.

Contribution

It introduces the first truly subquadratic quantum algorithms for approximate edit distance and extends the framework to distributed MapReduce settings.

Findings

Quantum algorithms achieve $O(n^{1.858})$ and $O(n^{1.781})$ runtimes with constant approximation factors.

MapReduce algorithm approximates edit distance within factor 3 using sublinear resources.

Framework enables efficient approximation in parallel and distributed environments.

Abstract

The edit distance between two strings is defined as the smallest number of insertions, deletions, and substitutions that need to be made to transform one of the strings to another one. Approximating edit distance in subquadratic time is "one of the biggest unsolved problems in the field of combinatorial pattern matching". Our main result is a quantum constant approximation algorithm for computing the edit distance in truly subquadratic time. More precisely, we give an $O(n^{1.858})$ quantum algorithm that approximates the edit distance within a factor of $7$. We further extend this result to an $O(n^{1.781})$ quantum algorithm that approximates the edit distance within a larger constant factor. Our solutions are based on a framework for approximating edit distance in parallel settings. This framework requires as black box an algorithm that computes the distances of several smaller strings all at once. For a quantum algorithm, we reduce the black box to \textit{metric estimation} and provide efficient algorithms for approximating it. We further show that this framework enables us to approximate edit distance in distributed settings. To this end, we provide a MapReduce algorithm to approximate edit distance within a factor of $3$, with sublinearly many machines and sublinear memory. Also, our algorithm runs in a logarithmic number of rounds.