Quantum algorithms for supervised and unsupervised machine learning

TL;DR

This paper introduces quantum algorithms for supervised and unsupervised machine learning that significantly speed up clustering tasks by leveraging quantum computing's ability to handle high-dimensional data efficiently.

Contribution

It presents novel quantum algorithms for cluster assignment and finding, achieving exponential speed-up over classical methods in high-dimensional spaces.

Findings

Quantum algorithms operate in logarithmic time relative to data size and dimension.

Demonstrates exponential speed-up over classical clustering algorithms.

Applicable to large-scale high-dimensional data analysis.

Abstract

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take time polynomial in the number of vectors and the dimension of the space. Quantum computers are good at manipulating high-dimensional vectors in large tensor product spaces. This paper provides supervised and unsupervised quantum machine learning algorithms for cluster assignment and cluster finding. Quantum machine learning can take time logarithmic in both the number of vectors and their dimension, an exponential speed-up over classical algorithms.