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Course Outline
Introduction to Quantum-AI Integration
- Motivations for hybrid quantum-classical intelligence
- Key opportunities and current technological barriers
- Positioning Google Willow within the quantum-AI landscape
Google Willow Architecture and Capabilities
- System overview and toolchain structure
- Supported quantum operations and feature set
- APIs for advanced experimentation
Hybrid Quantum-Classical Models
- Partitioning tasks between quantum and classical components
- Data encoding strategies for quantum-enhanced learning
- State preparation and measurement workflows
Quantum Machine Learning Algorithms
- Variational quantum circuits for AI tasks
- Quantum kernels and feature maps
- Optimization loops for hybrid models
Building Quantum-AI Pipelines with Willow
- Developing hybrid models end-to-end
- Combining Willow with TensorFlow Quantum
- Testing and validating quantum-AI prototypes
Performance Optimization and Resource Management
- Noise-aware AI model development
- Managing compute constraints in hybrid systems
- Benchmarking quantum-AI performance
Applications and Emerging Use Cases
- Quantum-enhanced data analysis
- AI-driven optimization with quantum acceleration
- Cross-industry adoption potential
Future Trends in Quantum-AI Convergence
- Roadmaps for large-scale quantum-AI systems
- Architectural advances and hardware evolution
- Research directions shaping the quantum-AI frontier
Summary and Next Steps
Requirements
- An understanding of quantum computing concepts
- Experience with machine learning frameworks
- Familiarity with hybrid quantum-classical workflows
Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers
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!
