# A Quantum Annealing-Based Approach to Extreme Clustering

## TL;DR

This paper introduces a distributed quantum annealing method for extreme clustering, enabling faster and near-optimal clustering of large datasets with many clusters, outperforming traditional algorithms in speed.

## Contribution

The paper presents a novel distributed quantum annealing approach for extreme clustering, demonstrating its efficiency and optimality under certain conditions.

## Key findings

- Achieves near-optimal clustering assignments
- Operates significantly faster than classical algorithms
- Effective for large-scale datasets with many clusters

## Abstract

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster.