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 Duration 21 hours

Course Outline

Introduction to Quantum-AI Integration

  • Drivers for hybrid quantum-classical intelligence
  • Key opportunities and existing technological constraints
  • Positioning Google Willow within the broader quantum-AI ecosystem

Google Willow Architecture and Capabilities

  • System overview and toolchain composition
  • Supported quantum operations and feature capabilities
  • APIs for advanced experimentation

Hybrid Quantum-Classical Models

  • Distributing tasks between 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
  • Optimization loops for hybrid model performance

Building Quantum-AI Pipelines with Willow

  • Constructing end-to-end hybrid models
  • Integrating Willow with TensorFlow Quantum
  • Testing and validating quantum-AI prototypes

Performance Optimization 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 analysis
  • AI-driven optimization utilizing quantum acceleration
  • Potential for cross-industry adoption

Future Trends in Quantum-AI Convergence

  • Roadmaps for large-scale quantum-AI deployments
  • Architectural advancements and hardware evolution
  • Research directions defining the quantum-AI frontier

Summary and Next Steps

Requirements

  • A solid grasp of quantum computing fundamentals
  • Proficiency with machine learning frameworks
  • Working knowledge of hybrid quantum-classical workflows

Target Audience

  • AI engineers
  • Machine learning specialists
  • Quantum computing researchers

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