Quantum & AI
Explore the intersection of quantum computing and AI. Learn quantum machine learning, quantum neural networks, and quantum-enhanced AI algorithms.
Prerequisites
Complete Level 7: Quantum Applications
🎯What You'll Learn
- ✓Quantum feature spaces and kernel methods
- ✓Variational quantum classifiers
- ✓Quantum neural networks
- ✓Quantum generative models
- ✓Quantum advantage in machine learning
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Quantum Neural Networks
Build quantum neural networks leveraging quantum parallelism for learning.
Quantum Kernel Methods
Learn quantum kernel methods for classification using quantum feature spaces.
Quantum Generative Models
Explore quantum generative adversarial networks for data generation.
Quantum PCA
Implement quantum Principal Component Analysis for exponential speedup.
Quantum Support Vector Machines
Master quantum support vector machines for classification in high-dimensional spaces.
QML Algorithms Overview
Overview of quantum machine learning algorithms: classification, clustering, regression.
Quantum Data Encoding
Learn techniques to encode classical data into quantum states.
Quantum Feature Maps
Design quantum feature maps transforming data for quantum machine learning.
Hybrid Classical-Quantum Models
Build hybrid models combining classical and quantum computation for practical applications.
Quantum Advantage in ML
Understand when quantum machine learning offers advantage over classical methods.