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Duration 21 hours
Course Outline
Foundations of Quantum-AI Integration
- Drivers for hybrid quantum-classical intelligence
- Significant opportunities and existing technical hurdles
- Contextualizing Google Willow within the quantum-AI ecosystem
Google Willow: Architecture and Features
- High-level system view and toolchain configuration
- Supported quantum operations and functional scope
- APIs facilitating advanced experimentation
Hybrid Quantum-Classical Modeling
- Distributing tasks across quantum and classical components
- Data encoding methods 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 cycles for hybrid models
Constructing Quantum-AI Pipelines with Willow
- End-to-end development of hybrid models
- Integrating Willow with TensorFlow Quantum
- Testing and validating quantum-AI prototypes
Performance Tuning and Resource Management
- Developing AI models with noise awareness
- Navigating compute constraints in hybrid systems
- Performance benchmarking for quantum-AI solutions
Applications and Emerging Use Cases
- Data analysis enhanced by quantum computing
- AI-driven optimization accelerated by quantum capabilities
- Potential for cross-industry implementation
Future Trajectories in Quantum-AI Convergence
- Strategies for large-scale quantum-AI systems
- Advancements in architecture and hardware evolution
- Research paths defining the quantum-AI frontier
Conclusion and Path Forward
Requirements
- A solid grasp of quantum computing principles
- Proficiency with machine learning frameworks
- Knowledge of hybrid quantum-classical workflows
Target Audience
- AI engineers
- Machine learning specialists
- Quantum computing researchers
