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Duration 21 hours
Course Outline
Foundations of Quantum-AI Integration
- Drivers behind hybrid quantum-classical intelligence
- Current opportunities and technological challenges
- The role of Google Willow in the quantum-AI ecosystem
Google Willow: Architecture and Core Features
- System architecture and toolchain overview
- Available quantum operations and feature capabilities
- Advanced experimentation via APIs
Developing Hybrid Quantum-Classical Models
- Strategic task partitioning between quantum and classical components
- Data encoding techniques for quantum-enhanced learning
- Workflows for state preparation and measurement
Quantum Machine Learning Algorithms
- Application of variational quantum circuits in AI
- Leveraging quantum kernels and feature maps
- Refining optimization loops within hybrid models
Constructing Quantum-AI Pipelines with Willow
- End-to-end development of hybrid models
- Integration of Willow with TensorFlow Quantum
- Prototyping, testing, and validation of quantum-AI solutions
Optimizing Performance and Resources
- Developing AI models with noise-awareness
- Navigating compute constraints in hybrid environments
- Performance benchmarking for quantum-AI systems
Real-World Applications and Use Cases
- Enhanced data analysis through quantum computing
- AI optimization accelerated by quantum processing
- Potential for cross-industry implementation
Trajectories in Quantum-AI Convergence
- Roadmaps for large-scale quantum-AI deployment
- Advances in architecture and hardware evolution
- Research trends defining the future of quantum-AI
Conclusion and Forward Path
Requirements
- A solid grasp of fundamental quantum computing principles
- Proficiency with established machine learning frameworks
- Practical familiarity with hybrid quantum-classical operational workflows
Target Professionals
- AI Engineers
- Machine Learning Specialists
- Quantum Computing Researchers