Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map

TL;DR

This paper presents a quantum-assisted clustering algorithm using the self-organizing feature map, demonstrating reduced computational complexity and error rates on quantum hardware, advancing unsupervised machine learning techniques.

Contribution

It introduces a novel hybrid quantum-classical algorithm for data clustering that reduces computational complexity and error rates compared to classical methods.

Findings

Quantum algorithm scales as O(LN), faster than classical O(LMN)

Proof-of-concept demonstrated on IBM quantum computer

Error in distance matrix decreases exponentially with runs

Abstract

Unsupervised machine learning is one of the main techniques employed in artificial intelligence. We introduce an algorithm for quantum-assisted unsupervised data clustering using the self-organizing feature map, a type of artificial neural network. The complexity of our algorithm scales as O(LN), in comparison to the classical case which scales as O(LMN), where N is the number of samples, M is the number of randomly sampled cluster vectors, and L is the number of the shifts of cluster vectors. We perform a proof-of-concept demonstration of one of the central components on the IBM quantum computer and show that it allows us to reduce the number of calculations in the number of clusters. Our algorithm exhibits exponential decrease in the errors of the distance matrix with the number of runs of the algorithm.