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Duration 21 hours
Course Outline
Introduction to Quantum-AI Integration
- Drivers for hybrid quantum-classical intelligence
- Key opportunities and existing technological hurdles
- The role of Google Willow in the quantum-AI ecosystem
Google Willow Architecture and Capabilities
- System overview and toolchain architecture
- Supported quantum operations and feature sets
- APIs for advanced experimental work
Hybrid Quantum-Classical Models
- Task partitioning between quantum and classical components
- Data encoding strategies for quantum-enhanced learning
- State preparation and measurement protocols
Quantum Machine Learning Algorithms
- Variational quantum circuits for AI applications
- Quantum kernels and feature mapping techniques
- Optimization loops for hybrid model refinement
Building Quantum-AI Pipelines with Willow
- End-to-end development of hybrid models
- Integrating Willow with TensorFlow Quantum
- Testing and validation of quantum-AI prototypes
Performance Optimization and Resource Management
- Development of noise-aware AI models
- Navigating compute constraints in hybrid systems
- Benchmarking performance in quantum-AI environments
Applications and Emerging Use Cases
- Quantum-accelerated data analysis
- AI-driven optimization leveraging quantum speedups
- Potential for cross-industry adoption
Future Trends in Quantum-AI Convergence
- Roadmaps for scalable quantum-AI systems
- Architectural innovations and hardware advancements
- Key research directions defining the quantum-AI frontier
Summary and Next Steps
Requirements
- A solid grasp of fundamental quantum computing principles
- Proficiency with machine learning frameworks
- Experience with hybrid quantum-classical workflows
Target Audience
- AI Engineers
- Machine Learning Specialists
- Quantum Computing Researchers
