🧠 Training: Quantum Machine Learning
Curious about how quantum computing can revolutionize Machine Learning?
This training offers an immersive experience into the concepts and tools of Quantum Machine Learning (QML), with a mix of theoretical and practical approaches.
🌀 Format: Hybrid (on-site and online)
👉 Indicate your preference directly on the registration form at the top right of this page.
🎯 Objectives
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Understand the analogies between classical probabilities and quantum states
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Discover quantum machine learning models and methods
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Learn how to implement and test QML algorithms on real datasets
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Identify current challenges and research perspectives in the field
📚 Training content
Introduction to Quantum Machine Learning
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Quantum states and measurements vs classical probabilities
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Datasets, encoding, and models
Variational models
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Building a quantum classifier
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Quantum neural networks
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Quantum kernel estimation (QKE)
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Definition and properties of quantum kernels
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Quantum Support Vector Machines (SVMs)
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Practical exercises
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Train a quantum model on a real dataset
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Build a Quantum Support Vector Machine
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Design and experiment with variational circuits
👥 Target audience
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PhD students, researchers, and faculty in computer science, physics, mathematics, or engineering
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Engineers and professionals interested in quantum applications in AI
✅ Teaching methods
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Theoretical lectures
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Demonstrations
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Programming sessions and hands-on exercises
📍 Location and format
Université de Reims Champagne-Ardenne – Moulin de la Housse Campus, Building 7, Room 702, 9:00 AM–5:00 PM
💻 Hybrid training: on-site & online
💡 Practical information
This training is offered free of charge by Eviden as part of the EuroCC2 project (CC-FR), MesoNet, and MaQuEst.
👉 Registration required (limited spots).
