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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 structure.
- Supported quantum operations and feature set.
- APIs designed for advanced experimentation.
Hybrid Quantum-Classical Models
- Strategies for partitioning tasks between quantum and classical components.
- Data encoding techniques 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.
- Optimization loops tailored for hybrid models.
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
- Developing AI models with noise awareness.
- Managing compute constraints within hybrid systems.
- Benchmarking quantum-AI performance metrics.
Applications and Emerging Use Cases
- Quantum-enhanced data analytics.
- AI-driven optimization accelerated by quantum processing.
- 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 foundational understanding of quantum computing principles.
- Practical experience with machine learning frameworks.
- Familiarity with hybrid quantum-classical workflows.
Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers