Tutorial: Introduction to Quantum Machine Intelligence

 Tutorial Abstract

This tutorial offers a hands-on, immersive introduction to the dynamic intersection of quantum machine learning (QML) and computational intelligence. It begins with a fundamental overview of quantum information science (QIS), covering key components such as qubits, quantum gates, measurements, and entanglement. 

From there, the session advances to core QML principles, exploring parameterized quantum circuits, data encoding techniques, and quantum circuit design methodologies. Participants will dive into a range of QML models, including quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum convolutional neural networks (QCNN). 

The tutorial also pushes into the frontier of QML with advanced models like quantum recurrent neural networks (QRNN) and quantum reinforcement learning (QRL). Notably, it highlights the relevance of quantum models in computational intelligence, such as in multi-agent quantum reinforcement learning, illustrating their applicability in real-world, multi-agent scenarios. 

Through practical programming examples and demonstrations using open-source quantum simulators, attendees will gain concrete insights into how QML can enhance computational intelligence tasks. Designed for beginners, the tutorial provides a clear path for those eager to integrate quantum techniques into their research. It also offers guidance on advanced learning resources, software packages, and frameworks to extend their exploration 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 Distributed/Federated Learning

  4. Challenges in Quantum Machine Learning

  5. Concluding Remarks

 Tutorial Formats

The tutorial will combine slides, handouts, and online materials for theoretical aspects. The hands-on portion will involve coding examples on a quantum computer simulator. 

We will provide our hands-on coding examples with two of the most commonly used open-source quantum computing simulator: PennyLane and Qiskit. Our code will be shared using Google colab so that there is no need for audiences to install virtual environments on their personal laptops to attend the tutorial. We will only use open-source software in this tutorial.

Further Informaion

2025 IEEE SSCI - Trondheim, Norway (ieee-ssci.org)