This course provides an in-depth examination of advanced Quantum Machine Learning (QML) algorithms, mathematical frameworks, and practical implementation strategies for complex problems. It covers quantum kernel methods, quantum neural networks, variational quantum algorithms for ML, and techniques for data encoding and measurement in quantum systems.
Prerequisites Strong Quantum Computing, ML background
Level:
Quantum Kernel Methods
Analyze and implement advanced quantum kernel methods for classification and regression tasks.
Quantum Neural Networks
Design and evaluate various architectures of Quantum Neural Networks (QNNs).
Variational Quantum Algorithms for ML
Implement and optimize Variational Quantum Algorithms (VQAs) like VQE and QAOA for machine learning applications.
Quantum Data Encoding
Evaluate and apply sophisticated techniques for encoding classical data onto quantum states.
Quantum Measurement Strategies
Analyze the impact of different measurement schemes on QML model performance.
Hardware-Aware QML
Understand the challenges and strategies for implementing QML algorithms on near-term quantum hardware.
Advanced Optimization Techniques
Apply advanced classical and quantum optimization techniques tailored for QML models.
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