Cursusaanbod

Introduction to Quantum-AI Integration

  • Motivations for hybrid quantum-classical intelligence
  • Key opportunities and current technological barriers
  • Positioning Google Willow within the quantum-AI landscape

Google Willow Architecture and Capabilities

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

Hybrid Quantum-Classical Models

  • Partitioning tasks 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 tasks
  • Quantum kernels and feature maps
  • Optimization loops for hybrid models

Building Quantum-AI Pipelines with Willow

  • Developing hybrid models end-to-end
  • Combining 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

Vereisten

  • An understanding of quantum computing concepts
  • Experience with machine learning frameworks
  • Familiarity with hybrid quantum-classical workflows

Audience

  • AI engineers
  • Machine learning specialists
  • Quantum computing researchers
 21 Uren

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