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Duration 21 hours
Course Outline
Introduction to Quantum-AI Integration
- The rationale for hybrid quantum-classical intelligence
- Significant opportunities and existing technological challenges
- Contextualising Google Willow within the broader quantum-AI ecosystem
Google Willow Architecture and Capabilities
- System overview and toolchain configuration
- Supported quantum operations and feature set
- APIs for advanced experimentation
Hybrid Quantum-Classical Models
- Distributing tasks across quantum and classical components
- Data encoding strategies for quantum-enhanced learning
- State preparation and measurement processes
Quantum Machine Learning Algorithms
- Variational quantum circuits applied to AI tasks
- Quantum kernels and feature mapping techniques
- Optimisation loops for hybrid architectures
Building Quantum-AI Pipelines with Willow
- Developing hybrid models from end to end
- Integrating Willow with TensorFlow Quantum
- Testing and validating quantum-AI prototypes
Performance Optimisation and Resource Management
- Noise-aware development of AI models
- Managing compute constraints within hybrid systems
- Benchmarking quantum-AI performance metrics
Applications and Emerging Use Cases
- Quantum-enhanced data analytics
- AI-driven optimisation through quantum acceleration
- Potential for cross-industry adoption
Future Trends in Quantum-AI Convergence
- Roadmaps for large-scale quantum-AI systems
- Architectural innovations and hardware evolution
- Research vectors defining the quantum-AI frontier
Summary and Next Steps
Requirements
- A foundational grasp of quantum computing principles
- Hands-on experience with machine learning frameworks
- Proficiency in hybrid quantum-classical workflows
Intended Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers
