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Course Outline
Introduction to Hybrid AI-Quantum Systems
- Overview of quantum computing principles
- Key components of hybrid AI-quantum systems
- Applications of quantum AI across industries
Quantum Machine Learning Algorithms
- Quantum algorithms for machine learning: QML, variational algorithms
- Training AI models using quantum processors
- Comparison of classical AI vs. quantum AI approaches
Challenges in Hybrid AI-Quantum Systems
- Handling noise and error correction in quantum systems
- Scalability and performance limitations
- Ensuring integration with classical AI frameworks
Real-World Applications of Quantum AI
- Case studies of hybrid AI-quantum systems in industry
- Practical implementations with quantum computing platforms
- Exploring potential breakthroughs in quantum AI
Optimizing Quantum AI Workflows
- Managing hybrid classical-quantum workflows
- Maximizing resource utilization in quantum AI systems
- Integration of quantum AI with classical AI infrastructures
Hybrid AI-Quantum Systems for Specific Use Cases
- Quantum AI for optimization problems
- Use cases in drug discovery, finance, and logistics
- Quantum-enhanced reinforcement learning
Future Trends in AI and Quantum Computing
- Advancements in quantum hardware and software
- Future potential of quantum AI in various fields
- Opportunities for research and development in quantum AI
Summary and Next Steps
Requirements
- Advanced knowledge of AI and machine learning
- Familiarity with quantum computing principles
- Experience in algorithm development and model training
Audience
- AI researchers
- Quantum computing specialists
- Data scientists and machine learning engineers
21 Hours
