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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
Testimonials (1)
Quantum computing algorithms and related theoretical background know-how of the trainer is excellent. Especially I'd like to emphasize his ability to detect exactly when I was struggling with the material presented, and he provided time&support for me to really understand the topic - that was great and very beneficial! Virtual setup with Zoom worked out very well, as well as arrangements regarding training sessions and breaks sequences. It was a lot of material/theory to cover in "only" 2 days, wo the trainer had nicely adjusted the amount according to the progress related to my understanding of the topics. Maybe planning 3 days for absolute beginners would be better to cover all the material and content outlined in the agenda. I very much liked the flexibility of the trainer to answer my specific questions to the training topics, even additionally coming back after the breaks with more explanation in case neccessary. Big thank you again for the sessions! Well done!
