Quantum Machine Learning in Practice
This course brings together two powerful computing paradigms: Artificial Intelligence and Machine Learning on the one hand, and Quantum Computing on the other. It explores how Quantum Computing can complement Classical approaches to Machine Learning and Deep Learning, with a particular focus on where Quantum Algorithms may provide practical value. The course provides a journey through the evolution of Quantum Machine Learning (QML), from early theoretical approaches to the practical techniques and algorithms that can be explored on today’s quantum hardware and simulators. Participants will learn how classical data can be encoded into Quantum States and processed using Quantum Circuits, and how Quantum Algorithms can be integrated into Machine Learning workflows. The course also examines the role of AI in the development and operation of Quantum Computing systems, providing a broader perspective on the relationship between these two rapidly evolving fields. A strong emphasis is placed on practical application. Participants will work with Qiskit and apply the concepts through hands-on exercises, with a particular focus on Quantum Support Vector Machines (QSVMs), Quantum Neural Networks (QNNs), and Quantum Convolutional Neural Networks (QCNNs). By the end of the course, participants will have a solid understanding of the synergy between AI, Machine Learning, and Quantum Computing, including what can be done today, what remains primarily experimental, and where future opportunities may emerge.
What you will learn
- The fundamentals of Quantum Computing and Quantum Algorithms
- Practical Quantum Algorithms available today
- The key concepts and categories of Machine Learning and Artificial Intelligence
- How classical data can be encoded into Quantum States, Qubits, and Quantum Circuits
- How Quantum Machine Learning fits into the broader landscape of AI, data, and computation
- The most important Quantum Machine Learning algorithms and approaches
- The evolution and different waves of Quantum Machine Learning
- The distinction between theoretical, experimental, and practical QML approaches
- Current research results and practical outcomes from leading research teams
- The potential future role of Quantum Algorithms in Machine Learning
- How to implement and experiment with QML algorithms using Qiskit
- How Quantum Support Vector Machines, Quantum Neural Networks, and Quantum Convolutional Neural Networks work
Programme
- Quantum Computing versus Classical Computing
- Introduction to Machine Learning and AI
- Machine Learning and Deep Learning approaches
- Positioning Quantum Machine Learning in the world of data and computation
- The evolution and different waves of Quantum Machine Learning
- Data encoding and Quantum Feature Maps
- Theoretical approaches to Quantum Machine Learning
- Practical approaches to Quantum Machine Learning
- Quantum Support Vector Machines (QSVM)
- Quantum Neural Networks (QNN)
- Quantum Convolutional Neural Networks (QCNN)
- Current research and practical results
- Hands-on QML development with Qiskit
- Additional learning materials and resources