Hands-On Introduction to Quantum Machine Learning
DOI:
https://doi.org/10.32473/flairs.37.1.135478Abstract
This tutorial covers a hands-on introduction to quantum machine learning. Foundational concepts of quantum information science (QIS) are presented (qubits, single and multiple qubit gates, measurements, and entanglement). Building on that, foundational concepts of quantum machine learning (QML) are introduced (parametrized circuits, data encoding, and feature mapping). Then, QML models are discussed (quantum support vector machine, quantum feedforward neural network, and quantum convolutional neural network). All the aforementioned topics and concepts are examined using codes run on a quantum computer simulator. All the covered materials assume a novice audience interested in learning about QML. Further reading and software packages and frameworks are shared with the audience.
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Copyright (c) 2024 Muhammad Ismail, Mohamed Shaban, Samuel Yen-Chi Chen
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.