Get in Touch
 Duration 21 hours

Course Outline

Foundations of Quantum-AI Integration

  • Drivers behind 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 designed for advanced experimentation

Hybrid Quantum-Classical Modelling

  • 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
  • Optimisation loops for hybrid models

Constructing Quantum-AI Pipelines with Willow

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

Performance Optimisation and Resource Management

  • Developing noise-aware AI models
  • Managing compute constraints in hybrid systems
  • Benchmarking quantum-AI performance

Applications and Emerging Use Cases

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

Future Trends in Quantum-AI Convergence

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

Summary and Next Steps

Requirements

  • A foundational grasp 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

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

Related Categories