Tutorial: Hands-On Introduction to Quantum Machine Learning

 Tutorial Abstract

This tutorial offers a hands-on introduction into the captivating field of quantum machine learning (QML). Beginning with the bedrock of quantum information science (QIS)—including essential elements like qubits, single and multiple qubit gates, measurements, and entanglement—the session swiftly progresses to foundational QML concepts. Participants will explore parameterized or variational circuits, data encoding or embedding techniques, and quantum circuit design principles.

Delving deeper, attendees will examine various QML models, including the quantum support vector machine (QSVM), quantum feed-forward neural network (QNN), and quantum convolutional neural network (QCNN). Pushing boundaries, the tutorial delves into cutting-edge QML models such as quantum recurrent neural networks (QRNN) and quantum reinforcement learning (QRL), alongside privacy-preserving techniques like quantum federated machine learning, bolstered by concrete programming examples.

Throughout the tutorial, all topics and concepts are brought to life through practical demonstrations executed on a quantum computer simulator. Designed with novices in mind, the content caters to those eager to embark on their journey into QML. Attendees will also receive guidance on further reading materials, as well as software packages and frameworks to explore beyond the session.

 Tutorial Contents

•  Part 1

  1. Qubits, single and multiple qubit gates, measurements, entanglement

  2. Concepts of Quantum Machine Learning

  3. Parameterized or variational quantum circuits

  4. Data encoding or embedding and quantum circuit design

• Part 2

  1. Quantum Support Vector Machine

  2. Quantum Feed-forward Neural Network

  3. Quantum Convolutional Neural Network

• Part 3

  1. Quantum Recurrent Neural Networks

  2. Quantum Reinforcement Learning

  3. Quantum Federated Learning

  4. Challenges in Quantum Machine Learning

  5. Concluding Remarks