AI in Quantum Computing: Future of Hybrid AI-Quantum Systems

• Advanced knowledge of AI and machine learning
• Familiarity with quantum computing principles
• Experience in algorithm development and model training

Audience

• AI researchers
• Quantum computing specialists
• Data scientists and machine learning engineers

AI in Quantum Computing is the combination of classical AI techniques with quantum computing to solve complex problems. This course explores how hybrid AI-quantum systems enhance computational efficiency, scalability, and performance in various industries. Participants will learn about advanced algorithms, quantum machine learning, and real-world applications that extend beyond traditional AI.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to leverage quantum computing to enhance AI capabilities.

By the end of this training, participants will be able to:

• Understand hybrid AI-quantum systems and their potential applications.
• Implement quantum machine learning algorithms.
• Optimize AI models using quantum computing resources.
• Overcome challenges in scaling and integrating quantum AI systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Hybrid AI-Quantum Systems

• Overview of quantum computing principles
• Key components of hybrid AI-quantum systems
• Applications of quantum AI across industries

Quantum Machine Learning Algorithms

• Quantum algorithms for machine learning: QML, variational algorithms
• Training AI models using quantum processors
• Comparison of classical AI vs. quantum AI approaches

Challenges in Hybrid AI-Quantum Systems

• Handling noise and error correction in quantum systems
• Scalability and performance limitations
• Ensuring integration with classical AI frameworks

Real-World Applications of Quantum AI

• Case studies of hybrid AI-quantum systems in industry
• Practical implementations with quantum computing platforms
• Exploring potential breakthroughs in quantum AI

Optimizing Quantum AI Workflows

• Managing hybrid classical-quantum workflows
• Maximizing resource utilization in quantum AI systems
• Integration of quantum AI with classical AI infrastructures

Hybrid AI-Quantum Systems for Specific Use Cases

• Quantum AI for optimization problems
• Use cases in drug discovery, finance, and logistics
• Quantum-enhanced reinforcement learning

Future Trends in AI and Quantum Computing

• Advancements in quantum hardware and software
• Future potential of quantum AI in various fields
• Opportunities for research and development in quantum AI

Summary and Next Steps