Machine Learning for Quantum Computing Specialists

TL;DR

Quantum machine learning (QML) is an emerging field leveraging quantum computing for faster data processing and novel feature detection, with recent progress demonstrated through practical experiments on current quantum devices.

Contribution

This paper provides an overview of QML applications, terminology, and recent experimental progress, highlighting its potential and current limitations.

Findings

Demonstrated QML applications on quantum devices

Potential for faster training and unique feature maps

Current examples lack commercial scale

Abstract

Quantum machine learning (QML) is a promising early use case for quantum computing. There has been progress in the last five years from theoretical studies and numerical simulations to proof of concepts. Use cases demonstrated on contemporary quantum devices include classifying medical images and items from the Iris dataset, classifying and generating handwritten images, toxicity screening, and learning a probability distribution. Potential benefits of QML include faster training and identification of feature maps not found classically. Although, these examples lack the scale for commercial exploitation, and it may be several years before QML algorithms replace the classical solutions, QML is an exciting area. This article is written for those who already have a sound knowledge of quantum computing and now wish to gain a basic overview of the terminology and some applications of classical machine learning ready to study quantum machine learning. The reader will already understand the relevant relevant linear algebra, including Hilbert spaces, a vector space with an inner product.