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Duration 21 hours
Course Outline
Introduction to Quantum-AI Integration
- Drivers for adopting hybrid quantum-classical intelligence
- Key opportunities and existing technological hurdles
- Strategic positioning of Google Willow in the quantum-AI ecosystem
Google Willow Architecture and Capabilities
- Overview of the system and its toolchain architecture
- Supported quantum operations and available features
- APIs enabling advanced experimentation
Hybrid Quantum-Classical Models
- Strategies for partitioning tasks between quantum and classical components
- Data encoding approaches for quantum-enhanced learning
- Workflows for state preparation and measurement
Quantum Machine Learning Algorithms
- Application of variational quantum circuits to AI tasks
- Utilizing quantum kernels and feature maps
- 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 validating quantum-AI prototypes
Performance Optimization and Resource Management
- Developing noise-aware AI models
- Managing compute constraints within hybrid systems
- Benchmarking performance in quantum-AI contexts
Applications and Emerging Use Cases
- Data analysis enhanced by quantum computing
- AI-driven optimization powered by quantum acceleration
- Potential for cross-industry adoption
Future Trends in Quantum-AI Convergence
- Roadmaps for deploying large-scale quantum-AI systems
- Advances in architecture and hardware evolution
- Research directions defining the future of quantum-AI
Summary and Next Steps
Requirements
- A solid grasp of fundamental quantum computing concepts
- Hands-on experience with machine learning frameworks
- Working familiarity with hybrid quantum-classical workflows
Target Audience
- AI Engineers
- Machine Learning Specialists
- Quantum Computing Researchers