A Quick Introduction to Quantum Machine Learning for Non-Practitioners
Ethan N. Evans Dominic Byrne Matthew G. Cook
This paper provides an introduction to quantum machine learning, exploring
the potential benefits of using quantum computing principles and algorithms
that may improve upon classical machine learning approaches. Quantum computing
utilizes particles governed by quantum mechanics for computational purposes,
leveraging properties like superposition and entanglement for information
representation and manipulation. Quantum machine learning applies these
principles to enhance classical machine learning models, potentially reducing
network size and training time on quantum hardware. The paper covers basic
quantum mechanics principles, including superposition, phase space, and
entanglement, and introduces the concept of quantum gates that exploit these
properties. It also reviews classical deep learning concepts, such as
artificial neural networks, gradient descent, and backpropagation, before
delving into trainable quantum circuits as neural networks. An example problem
demonstrates the potential advantages of quantum neural networks, and the
appendices provide detailed derivations. The paper aims to help researchers new
to quantum mechanics and machine learning develop their expertise more
efficiently.