Quantum computing for pattern classification

TL;DR

This paper explores quantum computing's potential to enhance pattern classification in machine learning, introducing a quantum algorithm based on Hamming distance measurement and demonstrating its application to handwritten digit recognition.

Contribution

It presents a novel quantum pattern classification algorithm utilizing quantum Hamming distance measurement, extending classical methods with quantum information theory.

Findings

Quantum algorithm improves pattern classification accuracy

Demonstrated application on MNIST handwritten digits

Highlights advantages of quantum over classical approaches

Abstract

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.