Introduction to Quantum Machine Learning

Rongji Li, Juan Xu, Jiabin Yuan +1 more

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more

Leonardo Alchieri, Davide Badalotti, Pietro Bonardi +1 more

Muhammad Ismail, Mohamed Shaban, Samuel Yen-Chi Chen

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.

Samuel Yen-Chi Chen, Shinjae Yoo

Sandip Dey, Sourav De, Siddhartha Bhattacharyya

Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML.