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 Duration 21 hours

Course Outline

Introduction to Quantum-AI Integration

  • Rationale for hybrid quantum-classical intelligence
  • Key opportunities and current technological hurdles
  • Positioning Google Willow within the broader quantum-AI ecosystem

Google Willow Architecture and Capabilities

  • System overview and toolchain architecture
  • Supported quantum operations and feature set
  • APIs for advanced experimentation

Hybrid Quantum-Classical Models

  • Task partitioning between quantum and classical components
  • Data encoding strategies for quantum-enhanced learning
  • State preparation and measurement workflows

Quantum Machine Learning Algorithms

  • Variational quantum circuits for AI applications
  • Quantum kernels and feature mapping
  • Optimization loops for hybrid models

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 Optimization and Resource Management

  • Noise-aware AI model development
  • Managing compute constraints in hybrid systems
  • Benchmarking quantum-AI performance

Applications and Emerging Use Cases

  • Quantum-enhanced data analysis
  • AI-driven optimization with quantum acceleration
  • Cross-industry adoption potential

Future Trends in Quantum-AI Convergence

  • Roadmaps for large-scale quantum-AI systems
  • Architectural advances and hardware evolution
  • Research directions shaping the quantum-AI frontier

Summary and Next Steps

Requirements

  • Foundational knowledge of quantum computing principles
  • Practical experience with machine learning frameworks
  • Working familiarity with hybrid quantum-classical workflows

Target Audience

  • AI Engineers
  • Machine Learning Specialists
  • Quantum Computing Researchers

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