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Course Outline
Introduction to Quantum-AI Integration
- Drivers for hybrid quantum-classical intelligence
- Principal opportunities and existing technological hurdles
- Contextualizing Google Willow within the quantum-AI ecosystem
Google Willow Architecture and Capabilities
- System overview and toolchain composition
- Supported quantum operations and feature suite
- APIs for advanced experimentation
Hybrid Quantum-Classical Models
- Allocating 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 applications
- Quantum kernels and feature maps
- Optimization loops for hybrid models
Constructing Quantum-AI Pipelines with Willow
- End-to-end development of hybrid models
- Integrating 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
- A foundational grasp of quantum computing principles
- Practical experience with machine learning frameworks
- Familiarity with hybrid quantum-classical operational workflows
Target Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers
21 Hours
