Tutorial: Quantum Machine Intelligence and Fuzzy Learning

 Tutorial Abstract

This tutorial offers a hands-on, immersive introduction to the dynamic intersection of quantum machine learning (QML), quantum fuzzy neural networks (QFNNs), and computational intelligence. It begins with a foundational overview of quantum information science (QIS), covering essential concepts such as qubits, quantum gates, measurements, and entanglement. From there, the session delves into core QML principles, exploring parameterized quantum circuits, data encoding techniques, and quantum circuit design methodologies. Participants will engage with a range of QML models, including quantum neural networks (QNN), quantum convolutional neural networks (QCNN), and the cutting-edge Quantum Fuzzy Neural Networks (QFNN), which combine fuzzy logic with quantum circuits for robust data representation and analysis. 

Advanced topics include quantum recurrent neural networks (QRNN), quantum reinforcement learning (QRL), and their integration into computational intelligence frameworks, such as multi-agent quantum reinforcement learning. The tutorial emphasizes real-world applications, showcasing QFNNs and other quantum models in tasks like sentiment analysis and multi-agent decision-making scenarios. Through practical programming examples and demonstrations using open-source quantum simulators, attendees will gain actionable insights into how QML and QFNNs can enhance computational intelligence tasks. Designed for beginners, the tutorial offers a clear entry point into quantum techniques while providing guidance on advanced resources, software packages, and frameworks to support continued exploration.

 Tutorial Contents

•  Part 1

  1. Qubits, single and multiple qubit gates, measurements, entanglement (Quick Review)

  2. Concepts of Quantum Machine Learning

  3. Hybrid Quantum-Classical Paradigm

  4. Parameterized or variational quantum circuits

  5. Quantum Gradient Calculation

  6. Data encoding or embedding and quantum circuit design

• Part 2

  1. Quantum Feed-forward Neural Network

  2. Quantum Recurrent Neural Networks

  3. Quantum Reinforcement Learning

  4. Quantum Distributed/Federated Learning

• Part 3

  1. Quantum Fuzzy Neural Network

  2. Quantum Fuzzy Federated Learning

  3. Challenges in Quantum Machine Learning and Quantum Fuzzy Learning

  4. 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

FUZZ-IEEE 2025 Official Website