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Duration 21 hours
Course Outline
Foundations of Quantum-AI Integration
- Drivers behind hybrid quantum-classical intelligence
- Primary opportunities and existing technological hurdles
- Strategic positioning of Google Willow in the quantum-AI sector
Google Willow: Architecture and Capabilities
- System overview and toolchain composition
- Supported quantum operations and feature sets
- APIs designed for advanced experimentation
Hybrid Quantum-Classical Modeling
- Distributing workloads between quantum and classical units
- Encoding strategies for quantum-boosted learning
- State preparation and measurement protocols
Quantum Machine Learning Algorithms
- Variational quantum circuits for AI applications
- Quantum kernels and feature mapping techniques
- Optimization cycles for hybrid architectures
Architecting Quantum-AI Pipelines with Willow
- End-to-end development of hybrid models
- Integration of Willow with TensorFlow Quantum
- Validation and testing of quantum-AI prototypes
Performance Tuning and Resource Governance
- Developing AI models with noise awareness
- Navigating compute limits in hybrid environments
- Performance benchmarking for quantum-AI systems
Applications and Emerging Scenarios
- Data analysis enhanced by quantum processing
- AI-driven optimization accelerated by quantum capabilities
- Potential for cross-industry implementation
Future Trajectories in Quantum-AI Convergence
- Roadmaps for large-scale quantum-AI infrastructure
- Architectural innovations and hardware progression
- Key research areas defining the quantum-AI frontier
Conclusions and Forward Path
Requirements
- Solid grasp of quantum computing principles
- Practical experience with machine learning frameworks
- Proficiency in hybrid quantum-classical workflows
Intended Participants
- AI Engineers
- Machine Learning Specialists
- Quantum Computing Researchers