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Duration 21 hours
Course Outline
Introduction to Quantum-AI Integration
- The rationale for hybrid quantum-classical intelligence
- Key opportunities and existing technological barriers
- Positioning Google Willow within the broader quantum-AI ecosystem
Google Willow Architecture and Capabilities
- System overview and toolchain architecture
- Supported quantum operations and feature set
- 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
- Workflows for state preparation and measurement
Quantum Machine Learning Algorithms
- Variational quantum circuits applied to AI tasks
- Quantum kernels and feature mapping techniques
- Optimization loops for hybrid models
Building Quantum-AI Pipelines with Willow
- Developing end-to-end 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 computational constraints in hybrid systems
- Benchmarking quantum-AI performance metrics
Applications and Emerging Use Cases
- Quantum-enhanced data analysis techniques
- 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 quantum computing principles
- Practical experience with machine learning frameworks
- Working knowledge of hybrid quantum-classical workflows
Target Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers
