  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.
