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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
- Positioning Google Willow within the broader quantum-AI ecosystem
Google Willow Architecture and Capabilities
- System overview and toolchain composition
- Supported quantum operations and feature sets
- APIs designed for advanced experimentation
Hybrid Quantum-Classical Models
- Strategic partitioning of tasks between quantum and classical components
- Data encoding strategies for quantum-enhanced learning
- State preparation and measurement protocols
Quantum Machine Learning Algorithms
- Variational quantum circuits tailored for AI tasks
- Quantum kernels and feature mapping techniques
- Optimization loops for hybrid model architectures
Building 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 development of AI models
- Managing compute constraints within hybrid systems
- Benchmarking the performance of quantum-AI solutions
Applications and Emerging Use Cases
- Quantum-enhanced data analytics
- AI-driven optimization leveraging quantum acceleration
- Potential for cross-industry adoption
Future Trends in Quantum-AI Convergence
- Roadmaps for large-scale quantum-AI systems
- Architectural advancements and hardware evolution
- Research directions defining the quantum-AI frontier
Summary and Next Steps
Requirements
- A solid grasp of core quantum computing concepts
- Practical experience with leading machine learning frameworks
- Familiarity with hybrid quantum-classical workflow architectures
Target Audience
- AI Engineers
- Machine Learning Specialists
- Quantum Computing Researchers
